10 Most Powerful AI Chips Dominating the LLM Race

The rapid growth of generative AI, large language models (LLMs) and increasingly sophisticated on-device and data-centre AI workloads means that the underlying silicon matters more than ever. Whether you’re training a trillion-parameter model or deploying inference at the edge, the choice of chip influences performance, cost, energy-efficiency and scalability. 

In 2025, several manufacturers have introduced or ramped next-generation AI accelerators that push memory bandwidth, new precision formats (FP8/INT8), integration of NPUs (neural processing units) and chiplet architectures. Below are some of the most noteworthy AI chips driving the next wave of AI infrastructure.

AWS Trainium2

The AWS Trainium2 chip is a cloud-native AI accelerator introduced by Amazon Web Services for training and deploying large-scale language models. According to AWS, the Trainium2 chip offers roughly 30-40% better price-performance than GPU-based instances in similar large-model training scenarios.

The Trn2 instances are built with 16 Trainium2 chips, delivering up to 20.8 petaflops of compute performance. They are intended for training and deploying LLMs with billions of parameters. Trn2 UltraServers combine four Trn2 servers into a single system, offering 83.2 petaflops of compute for higher scalability. These new UltraServers feature 64 interconnected Trainium2 chips.

Trainium2 works best for companies already using AWS. For tasks that need very low latency or on-premise deployment, other accelerators might be a better choice.

Google TPU v7 (Ironwood)

Google’s TPU v7, also known as Ironwood, is built for large-scale AI inference on the cloud. A single pod of 9,216 chips delivers around 42.5 exaflops of computing power. The chip is twice as energy efficient as the previous version, comes with six times more high-bandwidth memory (192 GB per chip), and has 50% faster interconnect speeds of about 1.2 TB/s.

For companies already using Google Cloud, the TPU v7 offers a powerful and ready-to-use option for running big AI models without building their own hardware setup. However, as it’s cloud-only, users rely on Google’s infrastructure and may face regional or cost limitations. 

Cerebras Wafer-Scale Engine 3

Developed by Cerebras Systems, Wafer-Scale Engine 3 is fabricated on a 5 nm process and packs an astonishing 4 trillion transistors, making it the largest single AI processor in existence. Cerebras builds the entire wafer as one massive chip, eliminating the communication bottlenecks that usually occur between GPUs. The WSE-3 integrates around 900,000 AI-optimised cores and delivers up to 125 petaflops of performance per chip. 

It also includes 44 GB of on-chip SRAM, enabling extremely high-speed data access, while the supporting system architecture allows expansion up to 1.2 petabytes of external memory — ideal for training trillion-parameter AI models.

AMD Instinct MI355X 

The AMD Instinct MI355X is AMD’s flagship AI and high-performance computing (HPC) accelerator, built to rival NVIDIA’s most powerful chips. Based on the CDNA 4 architecture, it pushes the boundaries of memory capacity, bandwidth, and efficiency. Each GPU packs 288 GB of ultra-fast HBM3E memory and an impressive 8 TB/s bandwidth, enabling it to handle extremely large AI models and datasets effortlessly. 

AMD claims the MI355X delivers up to four times higher peak performance than its predecessor, the MI300X, across both training and inference tasks. In large-scale data centre deployments, it can be configured in multi-GPU clusters offering up to 2.3 TB of total memory across eight GPUs. 

NVIDIA GB300 Blackwell Ultra 

The NVIDIA GB300 Blackwell Ultra is the most powerful GPU in the Blackwell lineup. Fabricated on TSMC’s 4NP process, it uses a dual-die architecture connected via NVIDIA’s ultra-fast NV-HBI interface, delivering around 10 TB/s of chip-to-chip bandwidth. 

Each GPU features 160 streaming multiprocessors and over 20,000 CUDA cores, along with fifth-generation Tensor Cores that significantly boost training and inference speeds.

The GB300 comes equipped with 288 GB of HBM3E memory and a staggering 8 TB/s bandwidth, enabling it to process massive datasets and run trillion-parameter models efficiently. In large-scale configurations such as NVIDIA’s NVL72 system, 72 of these GPUs are paired with 36 Grace CPUs, forming a single rack that can deliver tens of petaflops of FP4, FP8, and FP16 compute performance. 

Apple M5 chip 

The Apple M5 chip is the latest addition to Apple’s in-house silicon lineup, bringing faster performance and stronger AI capabilities to Macs and iPads. Built using Apple’s 3-nanometre process, the M5 focuses on improving speed, efficiency, and on-device AI processing. 

It comes with a 10-core CPU that delivers around 15% better performance than the M4, and a 10-core GPU with built-in neural accelerators for AI and graphics tasks. 

M5’s unified memory bandwidth of 153GB/s—a 30% increase over M4—lets users run larger AI models entirely on device. This makes the M5 faster at handling creative workloads like video editing, 3D rendering, and real-time AI tools.

Microsoft Maia and Cobalt

The Microsoft Maia and Cobalt chips mark the company’s entry into custom silicon, built to power Azure’s growing AI and cloud workloads. The Maia series focuses on AI acceleration, optimised for training and inference of LLMs. 

The first chip, Maia 100, is based on TSMC’s 5nm process and includes a large die with HBM2e memory offering up to 1.8 TB/s bandwidth. It features custom tensor and vector cores, allowing faster and more efficient model processing within Azure data centres. Microsoft’s goal with Maia is to reduce reliance on third-party GPUs and achieve greater control over performance, cost, and scalability for its AI infrastructure.

The Cobalt series, on the other hand, is Microsoft’s Arm-based CPU line, built for general-purpose cloud computing. The Cobalt 100 comes with 128 Neoverse N2 cores and high-speed DDR5 support, optimised for energy efficiency and high throughput. 

Intel Gaudi 3 

The Intel Gaudi 3 is built to handle large-scale model training and inference. It uses a dual-chiplet design made with a 5nm process, giving it strong performance and efficiency. The chip includes 64 Tensor Processor Cores and 8 Matrix Multiplication Engines.

Each Gaudi 3 unit comes with 128GB of HBM2e memory and offers an impressive 3.7TB/s of bandwidth, along with 96MB of on-die memory for faster data access. It also supports 24 Ethernet ports (200Gbps each), allowing easy scaling across multiple servers without depending on proprietary connections.

Intel claims that Gaudi 3 delivers up to 50% faster training and 40% better energy efficiency compared to earlier or competing AI accelerators. 

Huawei Ascend 910C

The Huawei Ascend 910C is Huawei’s newest AI processor, developed as part of its effort to build a complete, self-reliant AI hardware ecosystem. It builds on the previous Ascend 910B and features a dual-chiplet design. 

Manufactured using a 7nm process, the chip delivers strong performance with better power efficiency. The Ascend 910C works seamlessly with Huawei’s MindSpore AI framework and Atlas AI servers, creating an integrated platform for both cloud and on-premises AI deployments. It also uses high-bandwidth memory (HBM) to improve data throughput, which is essential for training massive models efficiently.

Groq LPU (Language Processing Unit)

 The Groq LPU is a specialised AI processor built for ultra-fast inference of LLMs. Unlike GPUs, which rely on complex caching and memory hierarchies, the LPU uses a unique deterministic architecture that keeps data close to the processor, cutting down latency and improving efficiency. It includes large on-chip memory and delivers extremely high bandwidth, up to 80 terabytes per second, allowing models to run faster and more predictably.

The Groq LPU excels at real-time AI inference, making it ideal for chatbots, translation systems, and other applications that need quick responses. 

Qualcomm Snapdragon X Elite

The Snapdragon X Elite from Qualcomm brings advanced AI performance to laptops and portable devices. Built using a 4nm process, it combines a powerful 12-core Oryon CPU, an Adreno GPU, and a Hexagon NPU that can deliver up to 45 trillion operations per second (TOPS) for on-device AI tasks.

 It supports up to 64GB of LPDDR5x memory with fast data transfer speeds, allowing smooth handling of AI-powered features like language models, voice assistants, and creative tools directly on the device.

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