MediaTek Strengthens Premium Push With Gen AI Capabilities

  • The Dimensity 9300 is integrated with the 7th-gen APU 790 processor, which supports a broad generative AI ecosystem and models including Meta Llama 2, Baidu ERNIE 3.5 SE and Baichuan 2.
  • It can run with 13 billion parameters, which are scalable up to 33 billion, on-device at a processing speed of up to 20 tokens per second.
  • The Dimensity 9300 will debut with the vivo X100 smartphone in mid-November

MediaTek recently launched its third-generation premium chipset Dimensity 9300. The company has emerged as a strong competitor in the premium smartphone chipset market after launching the Dimensity 9000 in Q1 2022. Currently, MediaTek leads the low-mid segment and drives significant volumes both for 4G and 5G in this tier.
Adopting a non-traditional CPU core design, the Dimensity 9300 focuses on raw performance. It has four large cores (Cortex-X4) and four performance cores (Cortex-A720 cores), thus enabling it to excel in raw computing power and advanced AI capabilities.

Mediatek Dimensity 9300 Features

Dimensity 9300 key specifications

Like the last two generations, the Dimensity 9300 SoC is built on a TSMC 4nm process node. It is more efficient and performs better than the Dimensity 9200. There is a 40% improvement in the multi-core performance and a 15% improvement in the single-core performance. The Dimensity 9300 combines an octa-core CPU with the company’s second-generation hardware raytracing engine, enabling smartphones to achieve console-level global illumination effects at a smooth 60 FPS. Besides, the chipset supports seamless multitasking, allowing users to simultaneously play games and stream videos or watch a video while gaming.

  • Four ARM Cortex-X4 CPU. Prime core clocked at up to 3.25GHz
  • Four ARM Cortex-A720 CPU clocked at up to 2.0GHz

Mediatek Dimensity 9300 CPU Architecture

7th-gen APU 790 processor

The chip is equipped with MediaTek’s next-generation APU 790 processor, which reduces power consumption by 45% while improving performance. Its processing speed is eight times that of the APU 690. It also offers significant improvements in generative AI performance and energy efficiency for edge computing. The APU 790 is specifically designed for generative AI tasks, marking a substantial upgrade over its predecessor. It accelerates processing through the Transformer model and supports image generation within one second using Stable Diffusion. The APU 790 also supports large language models with up to 33 billion parameters. MediaTek has also implemented mixed-precision INT4 quantization technology and NeuroPilot memory hardware compression to optimize memory usage for large AI models.

Mediatek Dimensity 9300 APU

The Dimensity 9300 has a strong AI generative ecosystem, which supports language models like Llama 2, Baichuan 2 and Baidu AI LLM. It helps developers to efficiently deploy multi-modal generative AI applications for users.

Immortalis-G720 GPU

With the integration of ARM’s latest GPU, the Immortalis-G720, the Dimensity 9300 offers almost a 46% boost in GPU performance and 40% power reduction compared to the Dimensity 9200.

Mediatek Dimensity 9300 GPU

The Dimensity 9300 chipset supports the new Ultra HDR format in Android 14, improving mobile photography with vibrant images and compatible JPEG files. It also offers ambient light adaptive HDR recovery technology for enhanced photography. It supports 100% pixel-level autofocus, dual lossless zoom and 3-microphone HDR audio recording.

The chipset’s display system is equipped with the MiraVision Picture Quality (PQ) engine which dynamically adjusts the contrast, sharpness and color of primary objects, resulting in lifelike video experiences similar to high-end TVs. It uses on-device AI to detect primary objects and background images in real time.

Enhanced connectivity

The Dimensity 9300 offers Wi-Fi 7 speeds up to 6.5 Gbps and improved long-range connectivity with Xtra Range 2.0 Technology. It also enhances smartphone tethering speeds by up to three times using Multi-Link Hotspot technology. The Dimensity 9300 also supports up to three Bluetooth antennas and features dual Bluetooth flash connection technology for an ultra-low latency Bluetooth audio experience.

DRAM support and security

The Dimensity 9300 is the first SoC that supports the LPDDR5T up to 9600 Mbps. Also, it integrates two SUPs, one for boot security and one for computing security.

Dimensity 9300 vs Snapdragon 8 Gen 3

In terms of specifications, the Dimensity 9300 uses all big core architecture 4 prime cores (Cortex-X4) and 4 big cores (Cortex-A720), whereas the Snapdragon 8 Gen 3 uses one prime (Cortex-X4), five big (Cortex-A720) and two small cores (Cortex-A520). MediaTek with its all-big core design is addressing generative AI and gaming applications. On paper, the Dimensity 9300’s AI performance is competitive. The Dimensity 9300 supports large language models that can run with 13 billion parameters, whereas the Snapdragon 8 Gen 3 can run with 10 billion parameters on-device.

The fact that MediaTek now offers performance and efficiency gains that are comparable to Qualcomm’s latest-generation flagship offerings, shows MediaTek wants to directly compete with Qualcomm in the premium segment. Overall, this is going to be a win-win for the industry, as it will raise the bar and, in turn, benefit the end users.

A table showing the differences between Mediatek Dimensity 9300 and Qualcomm Snapdragon 8 Gen 3

Expected timeline

The vivo X100 will be the first smartphone to carry the Dimensity 9300 chipset. It will be available in the market by the end of 2023. We expect that the Dimensity 9300 will have better adoption among Chinese OEMs compared to the Dimensity 9200. China will be the first target market for smartphones with the Dimensity 9300, followed by India, SEA and Europe.

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Podcast #71: Key Takeaways from Qualcomm Snapdragon Summit 2023

Qualcomm recently hosted its annual Snapdragon Summit in Maui, Hawaii, where it made several interesting announcements. AI, more specifically on-device generative AI, was a key theme. Qualcomm demonstrated the AI-on-edge capabilities on both smartphone and laptop reference designs, and they were impressive.

Key announcements included the Snapdragon 8 Gen 3 mobile platform, and the all-new Snapdragon X Elite compute platform powered by Oryon CPU cores. Qualcomm also announced the S7 and S7 Pro Gen 1 sound platforms, with the Pro version adding the XPAN feature for streaming audio using Bluetooth and Wi-Fi. Lastly, we also saw Snapdragon Seamless, a technology to link multiple devices across OEMs and multiple operating systems for audio and peripherical connectivity and file transfer.

In the latest episode of “The Counterpoint Podcast”, Research Director Tom Kang and VP of Research Neil Shah discuss their key takeaways from the Snapdragon Summit 2023. They touch upon the industry’s transformation towards AI-driven experiences, partnerships, the reduced gap between chipset release and chipset adoption, and much more.

Click the play button to listen to the podcast

Podcast Chapter Markers

00:33 – Neil talks about how this year’s Snapdragon Summit was different than the previous ones.

01:39 – Tom adds his views on what was interesting at the Snapdragon Summit this year.

02:53 – Neil shares his views on the Snapdragon X Elite with on-device generative AI, and how it can be revolutionary for the AI PC industry.

05:06 – Tom talks about Meta’s Llama 2 model unlocking new AI capabilities and experiences.

05:53 – Neil touches on Qualcomm’s partnerships for optimizing over 30 AI models for the X Elite and Snapdragon 8 Gen 3 platforms.

07:01 – Tom talks about AI experiences showcased by Qualcomm at the summit, and OEMs launching new devices at the same time as the chipset launch.

09:01 – Neil talks about the Snapdragon X Elite, its Oryon core, and ARM-based chipsets from competitors like AMD, NVIDIA, and Intel.

Also available for listening/download on:


Qualcomm Snapdragon X Elite Unveiled: ARM-based SoC for Windows-powered AI PCs

  • Qualcomm’s new Snapdragon X Elite SoC targets the AI PC market as the PC market bottoms out.
  • Made on TSMC’s 4nm process node, the new Snapdragon X Elite features a powerful 12-core Oryon CPU.
  • Between the Oryon CPU, Adreno GPU, and the Hexagon NPU, the Snapdragon X Elite can deliver up to 75 TOPS of AI computing performance.

Qualcomm announced its latest Snapdragon X Elite compute platform at the 2023 Snapdragon Summit held in Hawaii on October 24-26, during which the company also launched the Snapdragon 8 Gen 3 mobile platform. The Snapdragon X Elite is an ARM-based processor designed for personal computers and aims at delivering powerful performance and better power efficiency along with cutting-edge on-device generative AI features.

Qualcomm looking to expand its ARM SoC Smartphone Success to the PC Platform

Based on its brand-new ARM CPU core ‘Oryon’, developed from its Nuvia acquisition, Qualcomm’s Snapdragon X Elite SoC is built on TSMC’s 4nm process node. The CPU uses ARM’s 8.7 instruction set and features 12 high-performance ‘Oryon’ cores clocked at 3.8GHz. There is also a dual-core boost feature offering peak clock speeds of up to 4.3GHz. Qualcomm says the Snapdragon X Elite can offer 2x faster CPU performance than the competition while consuming one-third of the power.

counterpoint qualcomm snapdragon x elite cpu config
Source: Qualcomm

Combined with 136GB/s LPDDR5x memory bandwidth, and up to a total of 42MB cache, including three 12MB L2 cache, and the remaining 6MB shared with the overall CPU. The SoC comes with an integrated Adreno GPU as well as Hexagon NPU and sensing hub to enable multiple AI functions. This is effectively moving AI inferencing to the network edge (laptop). The Adreno GPU supports DirectX, OpenCL, and Vulcan APIs.

Built for on-device AI experiences, the Hexagon NPU can deliver peak AI computing performance of 45 TOPS, which Qualcomm claims is 4.5 times faster than the competition. And between the CPU, GPU, and NPU, the SoC can offer peak AI computing performance of 75 TOPS.

The SoC was designed to run large language models on-device, with up to 13 billion parameters, thus offering the claimed fastest Stable Diffusion performance by a laptop chip on the market. It can generate 30 tokens per second for seven billion large language models (LLMs).

counterpoint qualcomm snapdragon x elite specs at a glance
Source: Qualcomm

The SoC is designed in a way that it can be used on laptops, tablets, and even desktop PCs. Qualcomm showcased a 12W fanless reference design, along with 23W, 45W, and even the one with 80W thermal design power (TDP). TDP measures the maximum amount of heat produced by the chip in terms of watts.

PC vendors including Lenovo, HP, Dell, Microsoft, and Acer are all working to bring Snapdragon X Elite-powered laptops to the market by mid-2024.

WATCH: “AI PC” Era Beckons with Snapdragon X Elite: Deep Dive with Qualcomm’s Kedar Kondap

Snapdragon X Elite Benchmarks Show Impressive Gains over Apple Silicon, Intel and AMD Processors

At the Snapdragon Summit, we also had an opportunity to take a sneak peek at the Snapdragon X Elite benchmarking, where Qualcomm tested the SoC on both Windows and Linux (using Geekbench 6.2). Cinebench, UL Procyon AI, Wildlife Extreme, Aztec Ruins, and PC Mark were some of the other popular benchmarks on which the Snapdragon X Elite was tested.

counterpoint qualcomm snapdragon x elite benchmark configs
Source: Qualcomm

The reference design laptops were loaded with some popular benchmarks to show how they performed over the competition. There were two TDP configurations — Config A laptop with 80W max and Config B laptop with 23W.

Below are some of the scores that Qualcomm shared:

counterpoint qualcomm snapdragon x elite benchmarks
Source: Qualcomm

ARM-based Laptops to Further Gain Share at x86’s Expense

Qualcomm’s Snapdragon X Elite SoC was built completely on an ARM IP structure, instead of the long-lasting x86 IP structure in the PC industry, which supports our view of the double-digit YoY percentage growth in ARM-based laptop shipments.

counterpoint ai pc market forecast

After Microsoft and Qualcomm’s exclusive agreement to develop ARM-based Windows-compatible chips expires in 2024, we are expecting more chip vendors to enter the market and that will make the PC market more competitive. Intel could face more challenges to its long-lasting dominance in the PC CPU market.

We believe there are still challenges for the ARM-based PC ecosystem and apps/software support. Software developers have spent decades and billions of dollars writing code for Windows that runs on the x86 architecture. Even if Microsoft smoothly migrates its Windows software portfolio to ARM-based processors, it could take a lot of time for the ARM-based ecosystem to see similar migrations and maturity.

AI PC to Drive Another Wave of Shipment Growth in 2024

In 2023, PC OEMs and chip vendors are all dedicated to developing products/solutions for the AI universe. Over the past five quarters, PC OEMs have been working hard to resolve inventory issues and had a hard time searching for a new growth engine for the PC business. Now, the AI PC market is witnessing a surge, underpinned by Intel and Qualcomm’s new PC CPU platform, which is just around the corner. These AI-enabled PC models will likely be available around mid-2024.

We now expect AI PCs to have an over 50% 10-year CAGR from 2020, and after 2026, they will dominate the PC market. Intel, Qualcomm, and other PC CPU makers are working closely with PC OEMs toward the next-generation mainstream models, marking a new chapter for the PC industry.

Qualcomm Snapdragon 8 Gen 3 Unveiled: On-Device Generative AI Takes Center Stage

  • On-device generative AI was a key theme of the launch.
  • Qualcomm has revamped AI Engine with support for LLM, LVM, and ASR to run solely on-device.
  • It can run 10 billion parameters on-device and LLM models at up to 20 tokens per second.

Qualcomm announced the next-generation Snapdragon 8 Gen 3 mobile platform at its annual Snapdragon Summit held on October 24-26 in Hawaii. One of the key highlights of the new system-on-a-chip (SoC) is that it is now capable of running accelerated AI computer engines on-device. The chipset extends the possibilities of generative AI and enables a rich user experience. The launch will strengthen Qualcomm’s lead in the hardware-based AI capabilities for mobile devices.

On-device Generative AI: The Key Theme

Qualcomm spent a lot of time talking about generative AI and how it will transform smartphone computing and user experiences. Last year, with the Snapdragon 8 Gen 2, the focus was on the Hexagon processor with Sensing Hub and on-device personalization. This year’s focus was on running Large Language Models (LLM), Language Vision Models (LVM), and transformer network-based automatic speech recognition (ASR) with up to 10 billion parameters running natively on-device.

Qualcomm has refreshed the AI engine with the Hexagon NPU showing double the performance growth and a 40% increase in the performance per watt to run the AI models. The chipset can now run up to 10 billion parameters on-device and LLM models at up to 20 tokens per second, removing the need to rely on the cloud for inferencing. Qualcomm has partnered with Meta to support Llama 2 and with Microsoft for Stable Diffusion. The Snapdragon 8 Gen 3, using Stable Diffusion on-device, can generate images in less than a second, without connecting to the internet. These are impressive capabilities for a low-power battery-operated device.

counterpoint qualcomm snapdragon 8 gen 3 on-device generative ai
Source: Qualcomm

WATCH: On-Device Generative AI: Text-to-Image in Under a Second

Qualcomm has also added support on the SDK level so that developers can include their own models. Xiaomi has added a model for running AI on-device with six billion parameters. It has also added a host of support for creators to use multi-modal generative AI like:

  • LLM-based assistants can help the creative process and summarize ideas for you.
  • LVM can bring these ideas to life with the use of fast diffusion technology. For instance, you can ask to generate a completely new image, say, a family of four on the beach eating burgers.

Cognitive AI, i.e. LLM and LVM together, give the best user experience. HONOR announced that the Magic6 series smartphones will be powered by the Snapdragon 8 Gen 3 SoC and will have on-device LLM with seven billion parameters.

counterpoint qualcomm snapdragon 8 gen 3 camera
Source: Qualcomm

These models are being used for a breakthrough in camera experiences like:

  • A video object eraser by Arcsoft, allows to remove unwanted objects, and video bombers from videos.
  • OEMs can even use two always-sensing cameras in the front and back enabling easy QR code scan and face unlock.
  • The neural network allows you to zoom out beyond the picture captured, thus adding missing parts to photos making it feel like it was captured by a wide-angle lens. Qualcomm demoed this in action, calling it Photo Expansion, and it looked promising.

With advancements in AI-generated content, Qualcomm has also partnered with Truepic to adopt the C2PA standard. It lets viewers know whether the image is genuine or AI-generated and uses Qualcomm mobile security to create a cryptographic seal that indicates a genuine image. As all these experiences are on-device, the data never leaves your device, thus increasing security.

counterpoint qualcomm snapdragon 8 gen 3 specs
Source: Qualcomm

Snapdragon 8 Gen 3: Key Specifications

The Snapdragon 8 Gen 3 SoC is built on the enhanced TSMC 4nm process node, just like the last two generations. It is impressive to see how Qualcomm still managed to increase the CPU performance by making it 30% faster, while also consuming 20% less power. This was achieved by using a tri-cluster eight-core CPU design featuring:

  • One ARM Cortex-X4based prime core clocked at up to 3.3GHz
  • Five ARM Cortex-720-based performance cores clocked between 3.0-3.2GHz
  • Two ARM Cortex-520-based efficiency cores clocked at up to 2.3GHz

In terms of graphics, Qualcomm claims that the new Adreno GPU is 25% faster in performance and 25% more power efficient. Overall power savings have improved by 10%, Qualcomm said.

counterpoint qualcomm snapdragon 8 gen 3 cpu
Source: Qualcomm

Qualcomm has also improved the hardware-based ray tracing by 40% and added support for Unreal Engine 5 Lumen. The new Snapdragon 8 Gen 3 is the first chipset to support this engine. There is also the Adreno Frame Motion Engine 2.0, which doubles the frame rate from 60fps to 120fps. Furthermore, with the Snapdragon Elite Gaming suite, the new SoC also supports 240fps gaming on a 240Hz display.

counterpoint qualcomm snapdragon 8 gen 3 elite gaming
Source: Qualcomm

Enhanced Connectivity: 5G Advanced Ready, Wi-Fi 7 and Dual Bluetooth

Qualcomm has added support for 5G advance using the Snapdragon X75 Modem-RF System, with hardware-based AI acceleration, making it another first for Qualcomm. This enables the Snapdragon 8 Gen 3 chipset to achieve better speeds, coverage, mobility, link robustness, and location accuracy. There is also the Qualcomm FastConnect 7800 Wi-Fi 7 platform that supports the High Band Simultaneous Multi-Link for faster speeds and low-latency performance.

OEM Partners and Expected Timeline

The Snapdragon 8 Gen 3 chipset has secured design wins among global OEMs, including ASUS, HONOR, iQOO, MEIZU, NIO, Nubia, OnePlus, OPPO, realme, Redmi, RedMagic, Sony, vivo, Xiaomi, and ZTE.

This is the first time that the launch of the chipset and device occurred simultaneously. Xiaomi took the limelight by launching the Xiaomi 14 series powered by the Snapdragon 8 Gen 3 SoC at the same time as the chipset launch. The Snapdragon 8 Gen 3 shipments will ramp up in Q1 2024 with wider adoption across the smartphone OEMs.

Qualcomm S7, S7 Pro Gen 1 Sound Platforms with AI-enhanced Audio and XPAN Technology

Along with the mobile platform, Qualcomm also announced the S7 and S7 Pro Gen 1 sound platforms that aim to offer a more advanced and personalized audio experience. Just like in the Snapdragon 8 Gen 3 mobile platform, AI follows a similar trajectory here as well. The sound platforms come with a dedicated AI coreto offer up to 100x higher AI performance, and five times more computing power compared to the previous generation.

counterpoint qualcomm snapdragon 8 gen 3 snapdragon sound s7 pro xpan
Source: Qualcomm

The dedicated AI cores are also used for audio curation including hearing loss compensation, low-latency DSP and to power the latest fourth-generation Adaptive ANC. The platforms support Bluetooth 5.4, and Bluetooth LE including Auracast Broadcast Audio.

The Qualcomm S7 Pro Gen 1 platform takes it up a notch by adding micro-power Wi-Fi connectivity to intelligently switch between Bluetooth and Wi-Fi to offer the whole home and building coverage with XPAN technology. It supports 2.4GHz, 5GHz, and even 6GHz Wi-Fi bands. Lastly, with Snapdragon Sound, the S7 Pro Gen 1 can offer data rates of up to 29Mpbs, enabling lossless music streaming over Wi-Fi up to 24-bit/192kHz.

Snapdragon Seamless: Enabling Interoperability Between Windows and Android Devices

The Android and Windows ecosystems are fragmented, which makes interoperability a challenge. In an attempt to make devices work better together, irrespective of the OEM, Qualcomm also announced Snapdragon Seamless, which will enable Windows PCs and Android devices to discover each other and communicate seamlessly.

counterpoint qualcomm snapdragon 8 gen 3 snapdragon seamless
Source: Qualcomm

Samsung, with its Galaxy ecosystem, is trying to offer a seamless connectivity experience, but that is restricted to its own devices. Qualcomm is trying to break that barrier by offering a multi-device, multi-connect experience. Snapdragon Seamless will unify all devices from TWS and laptops to PCs, tablets, and smartphones, allowing rich experiences such as earbuds switching, text copy/paste, and image and video drag and drop between devices. It eliminates the need to constantly pair and unpair devices. Qualcomm demoed this between HONOR devices, and it looks promising.

3GPP 5G NTN Standards Set To Dramatically Boost Mobile Satellite Addressable Market

Satellite communications is back in the limelight following the launch of Apple’s direct Satellite-to-Phone service earlier this year. Partnering with satellite operator Globalstar, the service provides SOS messaging for iPhone 14/15 users. Recently, the service was expanded to include roadside assistance via satellite as well. A host of similar services and partnerships have been announced between satellite operators and chip vendors/cellular operators during the past few months, including Inmarsat with Mediatek, Iridium with Qualcomm and most recently SpaceX with KDDI.

In addition to the incumbent operators, there are a number of new players such as AST SpaceMobile and Lynk Global. AST SpaceMobile has partnered with Rakuten Mobile and currently has one operational satellite in-orbit. It has been granted preliminary experimental licenses in Japan and in the US. Meanwhile Lynk launched a limited commercial “store-and-forward” service using three satellites in April. Both companies plan to launch full constellations over the next few years.

The Mobile Satellite Services (MSS) market has historically been a niche market due primarily to the fact that MSS is based on proprietary technologies. However, 3GPP is working with the satellite industry on a global standardized solution, called 5G Non-Terrestrial Networks (NTN). 5G NTN will enable seamless roaming between terrestrial and satellite networks, using largely standard cellular devices, i.e., eliminating the need for proprietary terminals and fragmented satellite constellations. This could dramatically increase the addressable market for mobile satellite services.

5G Non-Terrestrial Networks (NTN)

With the emergence of new Satellite-to-Phone services, there is now a widespread industry push to deploy NTN-based satellite networks as this would benefit the satellite industry and the wider mobile industry. However, 3GPP has been working on NTN for some time. For example, there has been an ongoing study on 5G NTN since 3GPP Release 15, while in 2022, 3GPP introduced two parallel workstreams in its Release 17 specifications addressing 5G satellite-based mobile broadband and low-complexity IoT use cases:

  • NR-NTN (New Radio NTN) – adapts the 5G NR framework for satellite communications, providing direct mobile broadband services as well as voice using standard apps. This will enable 5G phones operating on dedicated 5G NTN frequencies and existing sub-7GHz terrestrial frequencies to link directly with Release-17 compatible satellites. Release 17 also includes enhancements for satellite backhaul and the inclusion of 80MHz MSS uplink spectrum in L-band (1-2GHz) plus a similar amount of downlink spectrum in S-band (2-4GHz).
  • IoT-NTN – provides satellite support for low-complexity eMTC and NB-IoT devices, which expands the coverage for key use cases such as worldwide asset tracking (for example, air freight, shipping containers and other assets outside cellular coverage). IoT-NTN is designed for low data rate applications such as the transmission of sensor data and text messages.

Release 17 established the NR-NTN and IoT-NTN standards while the upcoming 5G Advanced Release 18 will introduce new capabilities, coverage/mobility enhancements and support for expanded spectrum bands. For example, there are plans to extend the NR-NTN frequency range beyond 10GHz by adding Fixed Satellite Services (FSS) spectrum in the 17.7-20.2GHz band for downlink and 27.5-30.0GHz for uplink.

Satellite IoT

Traditional mobile satellite operators such as Inmarsat, Iridium and Globalstar have been offering M2M/IoT type services for many years targeting various industry verticals, ranging from agriculture, construction and oil and gas to maritime, transportation and utilities. Some of the traditional FSS players, such as AsiaSat, Eutelsat and Intelsat, also offer M2M/IoT services over Ku or Ka bands.

Another player with a long history in satellite communications is San Diego-based chip vendor Qualcomm. The company was a founding partner and key technology provider in Globalstar and also developed satellite-based asset tracking service OmniTRACS. Qualcomm is still heavily involved in the satcom business and earlier this year announced Snapdragon Satellite, its Satellite-to-Phone service. More recently, it announced the availability of two Release 17 compatible GEO/GSO IoT-NTN satellite modems launched in collaboration with US-based Skylo, a NTN connectivity service provider, that enables cellular devices to connect to existing, proprietary satellite networks:

  • Qualcomm 212S Modem – a satellite-only IoT modem designed to enable stationary sensing and monitoring IoT devices to communicate with NTN-based satellites. The chipset is an ultra-low power device and can be powered from solar panels or supercapacitors.
  • Qualcomm 9205S Modem – enables IoT devices to connect to both terrestrial cellular and satellite networks and has integrated GNSS to provide location data. Typical applications include industrial applications requiring always-on, hybrid terrestrial and satellite connectivity for tracking assets such as agricultural machinery, shipping containers, livestock, etc.

Both devices are designed for low-power, cost optimized applications and support the Qualcomm Aware cloud platform, which provides real-time asset tracking and device management in off-grid, remote areas for IoT.

Most of the major chip vendors, such as MediaTek, Qualcomm and Sony Semiconductors, have already developed Release 17 compatible chipsets. This means that satellite-compliant 5G IoT devices could be available commercially by the end of 2023 and should become commonplace in 2024.

NTN Satellite Operators

Only a few NTN-based satellites have been launched to date. A noteworthy example is Spanish LEO operator Sateliot, the first company to deploy satellites complying with 3GPP’s Release 17 IoT-NTN standard. Sateliot currently has two satellites in orbit and recently carried out a successful roaming test between its satellite network and Telefonica’s 5G terrestrial network using an IoT device with a standard SIM card. Sateliot plans to start commercial activities in 2024. Ultimately, the company hopes to launch a total of 250 nanosatellites, which will enable it to offer global 5G IoT-NTN services.

No satellite operator presently supports 3GPP’s Release 17 NR-NTN standard for voice and data. Although AST SpaceMobile and Lynk Global have demonstrated two-way satellite-to-5G terrestrial communications, neither uses the NR-NTN standard, although they have plans to test the NR-NTN standard.

Satellite Déjà Vu?

Over two decades ago, the mobile satellite industry invested billions to launch a number of ground-breaking LEO-based voice and narrowband data constellations. Only a handful survived and even fewer have prospered. Will history repeat itself?

Although there are some parallels, Counterpoint Research believes that there are also some important differences this time. During the past 20 years, satellites have become much smaller, more capable and less expensive. Some of these satellites are based on CubeSat technology, which uses commercial, off-the-shelf (COTS) components, thus drastically reducing costs while accelerating time to market. This is particularly relevant to nanosatellites, many of whom are being developed to target the IoT-NTN market. Another important difference is that launch costs have decreased significantly due to the entry of new private launch companies, notably SpaceX.

Perhaps the most important differentiator between current and next-generation satellites, however, is that the latter will be based on 3GPP’s NTN standards. Historically, proprietary satellite systems have resulted in a limited range of low volume and hence expensive end user devices – a significant barrier to growth. As with 5G (and 4G before it), a common set of cellular-based standards will enable the mobile satellite industry – plus the vertical markets it serves – to benefit from the vast economies of scale of the cellular device ecosystem. This should result in higher volume chipset production, more affordable devices and services and hence a much larger market of end users. For instance, Sateliot estimates that the cost of satellite IoT connectivity will drop from hundreds of dollars per device per month to less than $10 per device per month.

Furthermore, the adoption of 5G NTN and its integration with terrestrial 5G will result in a truly seamless global telecoms network, with increased space segment capacity, resulting in more users benefiting from higher data rate services. This will lead to more applications and use cases thus creating more value-add for vertical market users. Clearly, this could lead to a significant expansion of the mobile satellite services market globally.

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Podcast #70: Qualcomm Driving On-device Generative AI to Power Intelligent Experiences at the Edge

Generative AI like ChatGPT and Google’s Bard have disrupted the industry. However, they are still limited to browser windows and smartphone apps, where the processing is done through cloud computing. That is about to change soon as Qualcomm Snapdragon-powered devices will soon be able to run on-device generative AI.

At MWC 2023, Qualcomm showcased Stable Diffusion on a Snapdragon 8 Gen 2-powered Android smartphone. The demo showed how a smartphone can generate a new image with text commands or even change the background, without connecting to the internet. Running generative AI apps directly on a device offers several advantages, including lower operational costs, better privacy, security, and reliability of working without internet connectivity.

ALSO LISTEN: Podcast #69: ChatGPT and Generative AI: Differences, Ecosystem, Challenges, Opportunities

In the latest episode of ‘The Counterpoint Podcast’, host Peter Richardson is joined by Qualcomm’s Senior Vice President of Product Management Ziad Asghar to talk about on-device generative AI. The discussion covers a range of topics from day-to-day use cases to scaling issues for computing resources and working with partners and the community to unlock new generative AI experiences across the Snapdragon product line.

Click the play button to listen to the podcast

You can read the transcript here.

Podcast Chapter Markers

01:35: Ziad starts by defining generative AI and comparing it with machine learning and other types of AI.

03:56: Ziad talks about AI experiences that are already present in Snapdragon-powered devices.

06:24: Ziad addresses the scaling issue for computing resources used to train large language models.

09:46: Ziad deep dives into the types of day-to-day applications for generative AI on devices like a smartphone.

13:34: Ziad talks about the hybrid AI model, involving both cloud interaction and edge.

15:43: Ziad on how Qualcomm is leveraging its silicon chip capabilities to unlock generative AI experiences.

19:20: Ziad on how Qualcomm is working with its ecosystem and the developer community.

21:57: Ziad touches on the privacy and security aspect with respect to on-device generative AI.

Also available for listening/download on:


Global Smartphone AP-SoC Market Share: Quarterly

Global Smartphone AP (Application Processor) Shipments Market Share: Q1 2022 to Q2 2023

Published Date: September 7, 2023

A repository of quarterly data for the global smartphone AP market based on smartphone AP/SoC shipment numbers.

Global Smartphone Application Processor (AP) Market Share: Q2 2023This data is based on the smartphone AP/SoC shipments

Note: Totals may not add up due to rounding

Global Smartphone Chipset Market Share (Q1 2022 – Q2 2023)
Brands Q1 2022 Q2 2022 Q3 2022 Q4 2022 Q1 2023 Q2 2023
Mediatek 36% 36% 35% 33% 33% 30%
Qualcomm 34% 32% 32% 19% 27% 29%
Apple 14% 13% 16% 28% 26% 19%
UNISOC 11% 11% 9% 11% 8% 15%
Samsung 5% 8% 8% 8% 4% 7%
1% 0% 0% 0% 0% 0%
Others 0% 0% 1% 1% 1% 1%

Source: Global Smartphone AP-SoC Shipments & Forecast Tracker by Model – Q2 2023


(Use the buttons below to download the complete chart)


Apple’s sales declined in Q2 2023 due to seasonality. Its Pro series did better.

MediaTek’s shipments increased slightly in Q2 2023 with reduced inventory levels and growing competition in the entry-level 5G smartphone market. New smartphone launches in the low-to-mid-end segments increased the shipments of Dimensity 6000 and Dimensity 7000 series. The Dimensity 9200 Plus was added to the premium tier.

Qualcomm’s shipments increased in Q2 2023 due to the Snapdragon 8 Gen 2’s adoption in Samsung’s flagship smartphones and by Chinese OEMs. The launch of Samsung’s Flip and Fold series also contributed to this growth. Qualcomm refreshed the Snapdragon 7 Gen 1, Snapdragon 6 Gen 1 and Snapdragon 4 Gen 1 series to gain some share back. However, the premium segment’s growth remained in focus.

Samsung’s shipments increased in Q2 2023. The Exynos 1330 and 1380’s launch added volumes to the low and mid-high segments.

UNISOC’s shipments grew in Q2 2023 after a weak Q1. It gained some share in the $100-$150 LTE segment. In H2 2023, as entry-level 5G smartphones pick up in regions like LATAM, SEA, MEA and Europe, UNISOC will gain some share.

For a more detailed smartphone AP-SoC shipments & forecast tracker, click below:

Global Smartphone AP-SOC Shipment & Forecast Tracker by Model – Q2 2023

This report tracks the smartphone AP/SoC Shipments by Model for all the vendors. The scope of this report is from the AP/SoC shipments from all the key vendors like Apple, Qualcomm, MediaTek, Huawei, Samsung, UNISOC and JLQ. We have covered all the main models starting from Q1 2020 to Q2 2023. We have also included a one-quarter forecast for Q3E 2023. This report will help you to understand the AP/SoC Market from the shipment perspective. Furthermore, we have also covered key specs for these AP/SoC covering market view by:

  • Network (4G/5G AP/SoC)
  • Foundry Details (like TSMC, Samsung. etc.)
  • Process node (5nm, 6nm, 8nm, etc.)
  • Manufacturing Process (FinFET, N7, N5, etc.)
  • CPU Cores Architecture and CPU Cores Count
  • Modem (External/Internal)
  • Modem Name
  • Secure Element Presence
  • Security Chip
  • AI Accelerator

For detailed insights on the data, please reach out to us at sales(at) If you are a member of the press, please contact us at press(at) for any media enquiries.

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Podcast #69: ChatGPT and Generative AI: Differences, Ecosystem, Challenges, Opportunities

Generative AI has been a hot topic, especially after the launch of ChatGPT by OpenAI. It has even exceeded Metaverse in popularity. From top tech firms like Google, Microsoft and Adobe to chipmakers like Qualcomm, Intel, and NVIDIA, all are integrating generative AI models in their products and services. So, why is generative AI attracting interest from all these companies?

While generative AI and ChatGPT are both used for generating content, what are the key differences between them? The content generated can include solutions to problems, essays, email or resume templates, or a short summary of a big report to name a few. But it also poses certain challenges like training complexity, bias, deep fakes, intellectual property rights, and so on.

In the latest episode of ‘The Counterpoint Podcast’, host Maurice Klaehne is joined by Counterpoint Associate Director Mohit Agrawal and Senior Analyst Akshara Bassi to talk about generative AI. The discussion covers topics including the ecosystem, companies that are active in the generative AI space, challenges, infrastructure, and hardware. It also focuses on emerging opportunities and how the ecosystem could evolve going forward.

Click to listen to the podcast

Click here to read the podcast transcript.

Podcast Chapter Markers

01:37 – Akshara on what is generative AI.

03:26 – Mohit on differences between ChatGPT and generative AI.

04:56 – Mohit talks about the issue of bias and companies working on generative AI right now.

07:43 – Akshara on the generative AI ecosystem.

11:36 – Akshara on what Chinese companies are doing in the AI space.

13:41 – Mohit on the challenges associated with generative AI.

17:32 – Akshara on the AI infrastructure and hardware being used.

22:07 – Mohit on chipset players and what they are actively doing in the AI space.

24:31 – Akshara on how the ecosystem could evolve going forward.

Also available for listening/download on:


5G Advanced and Wireless AI Set To Transform Cellular Networks, Unlocking True Potential

The recent surge in interest in generative AI highlights the critical role that AI will play in future wireless systems. With the transition to 5G, wireless systems have become increasingly complex and more challenging to manage, forcing the wireless industry to think beyond traditional rules-based design methods.

5G Advanced will expand the role of wireless AI across 5G networks introducing new, innovative AI applications that will enhance the design and operation of networks and devices over the next three to five years. Indeed, wireless AI is set to become a key pillar of 5G Advanced and will play a critical role in the end-to-end (E2E) design and optimization of wireless systems. In the case of 6G, wireless AI will become native and all-pervasive, operating autonomously between devices and networks and across all protocols and network layers.

E2E Systems Optimization

AI has already been used in smartphones and other devices for several years and is now increasingly being used in the network. However, AI is currently implemented independently, i.e. either on the device or in the network. As a result, E2E systems performance optimization across devices and network has not been fully realized yet. One of the reasons for this is that on-device AI training has not been possible until recently.

On-device AI will play a key role in improving the E2E optimization of 5G networks, bringing important benefits for operators and users, as well as overcoming key challenges. Firstly, on-device AI enables processing to be distributed over millions of devices thus harnessing the aggregated computational power of all these devices. Secondly, it enables AI model learning to be customized to a particular user’s personalized data. Finally, this personalized data stays local on the device and is not shared with the cloud. This improves reliability and alleviates data sovereignty concerns. On-device AI will not be limited to just smartphones but will be implemented across all kinds of devices from consumer devices to sensors and a plethora of industrial equipment.

New AI-native processors are being developed to implement on-device AI and other AI-based applications. A good example is Qualcomm’s new Snapdragon X75 5G modem-RF chip, which has a dedicated hardware tensor accelerator. Using Qualcomm’s own AI implementation, this Gen 2 AI processor boosts the X75’s AI performance more than 2.5 times compared to the previous Gen 1 design.

While on-device AI will play a key role in improving the E2E performance of 5G networks, overall systems optimization is limited when AI is implemented independently. To enable true E2E performance optimization, AI training and inference needs to be done on a systems-wide basis, i.e.  collaboratively across both the network and the devices. Making this a reality in wireless system design requires not only AI know-how but also deep wireless domain knowledge. This so-called cross-node AI is a key focus of 5G Advanced with a number of use cases being defined in 3GPP’s Release 18 specification and further use cases expected to be added in later releases.

Wireless AI: 5G Advanced Release 18 Use Cases

3GPP’s Release 18 is the starting point for more extensive use of wireless AI expected in 6G. Three use cases have been prioritized for study in this release:

  • Use of cross-node Machine Learning (ML) to dynamically adapt the Channel State Information (CSI) feedback mechanism between a base station and a device, thus enabling coordinated performance optimization between networks and devices.
  • Use of ML to enable intelligent beam management at both the base station and device, thus improving usable network capacity and device battery life.
  • Use of ML to enhance positioning accuracy of devices in both indoor and outdoor environments, including both direct and ML-assisted positioning.

Channel State Feedback:

CSI is used to determine the propagation characteristics of the communication link between a base station and a user device and describes how this propagation is affected by the local radio environment. Accurate CSI data is essential to provide reliable communications. With traditional model-based CSI, the user device compresses the downlink CSI data and feeds the compressed data back to the base station. Despite this compression, the signalling overhead can still be significant, particularly in the case of massive MIMO radios, reducing the device’s uplink capacity and adversely affecting its battery life.

An alternative approach is to use AI to track the various parameters of the communications link. In contrast to model-based CSI, a data driven air interface can dynamically learn from its environment to improve performance and efficiency. AI-based channel estimation thus overcomes many of the limitations of model-based CSI feedback techniques resulting in higher accuracy and hence an improved link performance. The is particularly effective at the edges of a cell.

Implementing ML-based CSI feedback, however, can be challenging in a system with multiple vendors. To overcome this, Qualcomm has developed a sequential training technique which avoids the need to share data across vendors. With this approach, the user device is firstly trained using its own data. Then, the same data is used to train the network. This eliminates the need to share proprietary, neural network models across vendors. Qualcomm has successfully demonstrated sequential training on massive MIMO radios at its 3.5GHz test network in San Diego (Exhibit 1).

Wireless AI
© Qualcomm Inc.

Exhibit 1: Realizing system capacity gain even in challenging non-LOS communication

AI-based Millimetre Wave Beam Management:

The second use case involves the use of ML to improve beam prediction on millimetre wave radios. Rather than continuously measuring all beams, ML is used to intelligently select the most appropriate beams to be measured – as and when needed. A ML algorithm is then used to predict future beams by interpolating between the beams selected – i.e. without the need to measure the beams all the time. This is done at both the device and the base station. As with CSI feedback, this improves network throughput and reduces power consumption.

Qualcomm recently demonstrated the use of ML-based algorithms on its 28GHz massive MIMO test network and showed that the performance of the AI-based system was equivalent to a base case network set-up where all beams are measured.

Precise Positioning:

The third use case involves the use of ML to enable precise positioning. Qualcomm has demonstrated the use of multi-cell roundtrip (RTT) and angle-of-arrival (AoA)-based positioning in an outdoor network in San Diego. The vendor also demonstrated how ML-based positioning with RF finger printing can be used to overcome challenging non-line of sight channel conditions in indoor industrial private networks.

An AI-Native 6G Air Interface

6G will need to deliver a significant leap in performance and spectrum efficiency compared to 5G if it is to deliver even faster data rates and more capacity while enabling new 6G use cases. To do this, the 6G air interface will need to accommodate higher-order Giga MIMO radios capable of operating in the upper mid-band spectrum (7-16GHz), support wider bandwidths in new sub-THz 6G bands (100GHz+) as well as on existing 5G bands. In addition, 6G will need to accommodate a far broader range of devices and services plus support continuous innovation in air interface design.

To meet these requirements, the 6G air interface must be designed to be AI native from the outset, i.e. 6G will largely move away from the traditional, model-driven approach of designing communications networks and transition toward a data-driven design, in which ML is integrated across all protocols and layers with distributed learning and inference implemented across devices and networks.

This will be a truly disruptive change to the way communication systems have been designed in the past but will offer many benefits. For example, through self-learning, an AI-native air interface design will be able to support continuous performance improvements, where both sides of the air interface — the network and device — can dynamically adapt to their surroundings and optimize operations based on local conditions.

5G Advanced wireless AI/ML will be the foundation for much more AI innovation in 6G and will result in many new network capabilities. For instance, the ability of the 6G AI native air interface to refine existing communication protocols and learn new protocols coupled with the ability to offer E2E network optimization will result in wireless networks that can be dynamically customized to suit specific deployment scenarios, radio environments and use cases. This will a boon for operators, enabling them to automatically adapt their networks to target a range of applications, including various niche and vertical-specific markets.

Related Posts:

White Paper: Growing 5G+Wi-Fi RF Complexity Demands Innovative, Advanced & Tightly Integrated RFFE Solutions

Growing 5G+Wi-Fi RF Complexity Demands Innovative, Advanced & Tightly Integrated RFFE Solutions


PDF | 15 pages
Published date: July 2023

The rising adoption of advanced multimode cellular (5G, 4G) and wireless (Wi-Fi 6/6E/7) delivers powerful benefits while also driving significant RF complexity in smart connected devices. 5G and Wi-Fi 7 integration has multiple challenges that need cutting-edge RF design, components and end-to-end optimization. There are multiple players in the RF Front-End (RFFE) ecosystem, but most are specialists in only one or a few areas.

This paper highlights the technology potential of these powerful wireless technologies, complexity it brings and how product designers and manufacturers can solve these complexities with an advanced, end-to-end optimized and integrated RFFE solution.

• Executive Summary
• Proliferating 5G+Wi-Fi 7 A Massive Opportunity
• 5G+Wi-Fi 7 Takes Wireless Performance to the Next Level
• 5G+Wi-Fi 7 Coexistence Brings RF Complexity
• 5G+Wi-Fi 7 Solutions for Potential Challenges to Performance Enhancement
• Key Takeaways

Neil Shah

Research Vice President


Parv Sharma

Senior Analyst



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