Comparison of HPC Accelerators 2026

Comparison of HPC Accelerators for 2026

August 10, 2026

Every year, companies introduce new and unique accelerators and solutions for HPC that drive major advancements in the development, research, and production of LLMs, generative AI, and complex scientific computations. That’s why we’ve summarized the most significant ones for 2026, compared their individual specifications and performance, and outlined their key features.

Overview

Computational accelerators now play a completely dominant role in HPC and AI data centers. Instead of having all work processed by traditional processors, computations are distributed across specialized units with a tailored instruction set, tensor cores, or extremely fast HBM memory to process complex matrix operations many times faster.

While modern server processors today reach up to 192 cores, computing accelerators contain thousands to hundreds of thousands of them. Furthermore, today’s market is shifting from standalone PCIe cards to massive rack-based architectures, interconnected chiplets, and entire silicon wafers.

Manufacturers are constantly pushing the boundaries of architecture, memory bandwidth, and interconnects. To help you better understand current trends and get a good overview of the available options, we’ve put together this comparison of the latest products on the market.

Individual accelerators

NVIDIA B300 / Vera Rubin (R100) AMD MI455X / 430X Intel Gaudi 3 Groq LPU Cerebras WSE-3 Huawei Ascend 950PR

Accelerator parameters

Company	NVIDIA	NVIDIA	AMD	AMD	Intel	Groq	Cerebras	Huawei
Accelerator	B300	Vera Rubin (R100)	MI455X	MI430X	Gaudi 3	Groq LPU	WSE-3	Ascend 950PR
Number of cores	20,480	N/A	256 WGP	TB/A	64 TPC	1 TSP	900,000	TB/A
Transistor count	208 billion	336 billion	320 billion	TB/A	TB/A	TB/A	4,000 billion	TB/A
Manufacturing process	5 nm	3 nm	2 nm	2 nm	5 nm	4 nm	5 nm	5 nm
Capacity	288 GB	288 GB	432 GB	432 GB	128 GB	230 MB	44 GB	112 GB
Memory type	HBM3e	HBM4	HBM4	HBM4	HBM2e	on-chip SRAM	on-chip SRAM	HiBL (prop.)
Bus	NVLink 5	NVLink 6	UALink / PCIe Gen 6	UALink / PCIe Gen 6	RoCE / PCIe Gen 5	PCIe/prop.	prop.	LinQu/HCCS
Consumption	1,400 W	2,300 W	900 W	900 W	900 W	70 W	27 kW	600 W
Price	53,000 USD*	N/A	TB/A	TB/A	15,625 USD*	N/A	N/A	9,600 USD*

* These specifications are only estimates and have not been verified; they may change over time

Comparison of accelerator performance

Company	NVIDIA	NVIDIA	AMD	AMD	Intel	Groq	Cerebras	Huawei
Accelerator	B300	Vera Rubin (R100)	MI455X	MI430X	Gaudi 3	Groq LPU	WSE-3	Ascend 950PR
FP16/BF16	2,250 TFLOPs	4,000 TFLOPs	5,000 TFLOPs	TB/A	459 / 1,835 TFLOPs	188 TFLOPs	125,000 TFLOPs	500 TFLOPs
FP8	4,500 TFLOPs	17,500 TFLOPs	20,100 TFLOPs	TB/A	1,835 TFLOPs	376 TFLOPs	N/A	1,000 TFLOPs
FP4/INT4	13,500 TFLOPs	50,000 TFLOPs FP4I, 35,000 TFLOPs FP4T*	40,300 TFLOPs	TB/A	N/A	N/A	156,250 TFLOPs	1,560 TFLOPs
INT8	187.5 TOPs	250 TOPs	5,000 TOPs	TB/A	N/A	750 TOPs	N/A	N/A

* FP4I refers to NVFP4 performance in terms of inference accuracy; FP4T refers to NVFP4 performance in terms of training.

NVIDIA B300 / Vera Rubin (R100)

The NVIDIA B300 (Blackwell Ultra) accelerator delivers the computing power needed for massively scaled systems designed for both training and inference of today’s most complex AI models. The B300 is built on an optimized Blackwell architecture (using TSMC’s 4NP process), which delivers multi-fold performance gains over the previous generation, Hopper, in a wide range of applications. With a massive 288 GB of HBM3e memory and bandwidth reaching 8 TB/s, it serves as the ideal backbone for deploying large language models (LLMs) and massive datasets.

In contrast, the Vera Rubin architecture with the R100 model represents not merely an evolution, but a completely new generational leap driven by the 3nm manufacturing process. Thanks to new computing engines, performance for AI applications is reaching entirely new levels while maintaining high energy efficiency. Furthermore, the Rubin R100 is the first on the market to feature revolutionary HBM4 memory with an incredible bandwidth of 22 TB/s, which addresses the historically greatest weakness of AI accelerators: the so-called “memory wall” (memory bottleneck).

LLM, Generative AI, Deep Learning, Machine Learning, Data Processing

Memory Access Speed: Vera Rubin vs. Blackwell

Both architectures represent the absolute cutting edge for solutions involving neural network training, massive data analytics, and the operation of Mixture-of-Experts (MoE) models, where extreme memory capacity and lightning-fast interconnections between individual nodes are critically important. A key innovation is full hardware support for 4-bit precision (FP4) computations, enabling the R100 to deliver up to 50 PFLOPS of inference throughput per card, which drastically reduces costs and accelerates the production deployment of AI.

In addition, both the B300 and R100 take full advantage of the latest iterations of NVLink interconnect technology, which is key to scalability. While the B300 uses 5th-generation NVLink with a data rate of 1.8 TB/s, the R100 pushes the boundaries with 6th-generation NVLink and a throughput of 3.6 TB/s per GPU. With the help of advanced NVLink Switch systems, dozens of chips can be interconnected across entire racks to form a single massive logical unit. Combined with high-speed networking and NVIDIA MagnumIO software, these solutions enable seamless, lossless scaling from smaller enterprise clusters all the way up to exascale supercomputers.

AMD Instinct MI455X

AMD MI455X Announcement

This AMD flagship for 2026, built on the new CDNA 5 architecture, is a direct response to NVIDIA’s competing Rubin platform. The chip consists of 12 chiplets utilizing TSMC’s advanced 2nm and 3nm manufacturing processes and contains a massive 320 billion transistors in total. The MI455X accelerator is designed primarily for large-scale server deployments; a typical example is the AMD Helios infrastructure, which combines a total of 72 of these cards into a single computing unit within a single rack.

Leads the Pack in HBM4 Memory Capacity

A key competitive advantage of the MI455X is its memory subsystem. The card features an unprecedented 432 GB of next-generation HBM4 memory with a bandwidth reaching an incredible 23.3 TB/s. In terms of capacity alone, it surpasses NVIDIA’s current solutions (288 GB for the B300 and R100 chips) by a full 50%. This advantage plays a critical role in the inference and training of the largest language models, which can now fit much more easily into the memory of a single card or a small number of cards without the need for complex partitioning. Raw computational performance in AI operations peaks at 40 PFLOPS for the FP4 data format.

AMD Instinct MI430X

While the MI455X model is focused purely on maximum performance for generative artificial intelligence, the MI430X variant (which is expected to be fully deployed commercially in 2027) targets traditional HPC, national supercomputers, and scientific institutions. Although the card retains the massive 432 GB of HBM4 memory and high bandwidth, the internal architecture of its computing units differs in its primary focus.

Top Performance for Scientific Simulations (FP64)

A key feature of the MI430X is its optimization for FP64 (double-precision) computations, which are absolutely essential for complex physical simulations, climate modeling, and materials development. In this regard, the card achieves a hardware performance of 288 TFLOPS, overwhelmingly surpassing all available alternatives on the market. The goal of this solution is to enable institutions to run the most demanding scientific simulations while simultaneously training AI models directly on the generated data within a single unified ecosystem.

Intel Gaudi 3

Intel’s third-generation AI accelerator, Gaudi 3, represents a significant technological leap forward compared to their predecessor thanks to the transition to a 5nm manufacturing process. The card, built on the standardized OAM (Open Accelerator Module) format, is equipped with 64 tensor cores and eight specialized matrix multiplication engines. Memory capacity has increased to 128 GB of HBM2e with a bandwidth of 3.7 TB/s, enabling the system to handle large language models much more smoothly.

A cost-effective alternative for AI training and inference

While Gaudi 2 was compared to the NVIDIA A100, the goal of the Gaudi 3 generation is to compete with the NVIDIA H100. Although it lags slightly behind its main rival in raw computing power (reaching 1,835 TFLOPS for BF16/FP8 operations), its main strength is cost-effectiveness. The chip is about half the price, yet delivers comparable results in a range of real-world AI applications, making it a highly attractive choice for building large-scale data centers and clusters with a focus on return on investment.

Integrated RoCEv2 Scaling

As with previous generations, the Gaudi platform’s greatest strength lies in its network integration. Gaudi 3 features twenty-four integrated 200-gigabit RDMA over Converged Ethernet (RoCEv2) ports directly on the chip, which is twice the speed of Gaudi 2. This built-in connectivity completely eliminates the need for expensive external network switches for intra-node communication, and thanks to the Ethernet industry standard, customers can scale their computing clusters seamlessly from eight accelerators in a single rack up to massive systems with thousands of units.

Groq LPU: A Revolution in Inference Speed

Although Groq had existed previously, it wasn’t until the massive rise of generative AI that it definitively established itself among the key players in the HPC market. Its solution, known as the LPU (Language Processing Unit), represents a completely different approach to accelerating artificial intelligence than traditional graphics processing units (GPUs). As the name suggests, LPU chips are not primarily designed for the computationally intensive training of new neural networks, but focus purely on inference—that is, running already trained large language models (LLMs) as quickly and efficiently as possible in real time.

The End of HBM Memory and Deterministic Architecture

The main difference from the competition lies in the memory architecture. Traditional accelerators from NVIDIA or AMD rely on external HBM memory. Although these memory modules are enormous and offer massive bandwidth, the constant transfer of data (model parameters) between the memory and the computing core causes an inevitable delay known as the “memory wall.” Groq radically circumvents this problem by completely eliminating the need for traditional external memory.

This LPU architecture (also known as the Tensor Streaming Processor) uses exclusively integrated on-chip SRAM, which is physically located right next to the computing units. The latest-generation Groq 3 LPU from 2026 features 500 MB of extremely fast memory per chip with a local bandwidth of an incredible 150 TB/s. As a result, it delivers deterministic hardware capable of generating text or code at a rate of hundreds of tokens per second with virtually zero latency.

Scaling for Real-Time AI

Since modern models with tens of billions of parameters obviously won’t fit into a single chip’s 500 MB of memory, the magic of the Groq LPU lies in its seamless interconnectivity. A standard computing node interconnects hundreds of these chips (typically 256 chips in a single rack) via a proprietary network. From the perspective of the software and compiler, the entire rack then behaves completely synchronously as a single massive processor. Optimized for the FP8 format, such a cluster achieves a performance of 315 PFLOPS, making it the uncompromising choice for deploying voice AI assistants, autonomous agents, and applications where immediate response is a critical factor.

Cerebras WSE-3

Cerebras WSE-3 Wafer-Scale System

In the case of Cerebras, this is a truly unique piece of technology—a gigantic chip the size of an entire silicon wafer with an area of 46,225 mm². In the third generation of this accelerator, which uses TSMC’s 5-nanometer manufacturing process, engineers have managed to pack 4 trillion transistors and 900,000 cores optimized for artificial intelligence.

The WSE (Wafer Scale Engine) concept offers significant advantages over traditional clustered systems. Since all computing power is concentrated on a single massive surface, communication paths between units are minimal, which significantly reduces latency compared to the standard interconnects that limit traditional graphics clusters. The chip achieves a peak AI performance of 125 petaFLOPS. At the same time, 44 GB of extremely fast SRAM memory is integrated onto the wafer itself, offering a massive data throughput of 21 petabytes per second.

Simplified Training of Gigantic Models

While in traditional GPU clusters, large language models must be distributed across thousands of cards in a highly complex manner that results in performance loss, the WSE-3 architecture (which forms the basis of CS-3 supercomputers) can seamlessly train AI models with up to 24 trillion parameters on a single system. For the most demanding operations and future models, the Cerebras solution allows up to 2,048 of these systems to be interconnected, creating an AI supercomputer with a staggering performance of 256 exaFLOPS.

The Chinese Market and Export Restrictions: The Absence of NVIDIA and the Rise of Domestic Chips

In recent years, the geopolitical situation has had a profound impact on the global market for HPC and AI accelerators. Due to strict export restrictions imposed by the U.S. government, NVIDIA (and other Western manufacturers) are now prohibited from supplying their most powerful chips to the Chinese market. While previously at least specially modified and downgraded models (such as the NVIDIA H20) were available on this market, current flagship models based on the Blackwell (B300) or Vera Rubin architectures are completely unavailable in China. Furthermore, in response to these sanctions, the Chinese government is actively pressuring local tech giants to completely cut themselves off from Western hardware.

Huawei Ascend 950PR: A New Chinese Flagship

This pressure created enormous opportunities for domestic manufacturers, and Huawei was the company that capitalized on them the most. In early 2026, it unveiled its new technological flagship—the Da Vinci 3.0 architecture with the accelerator Ascend 950PR. This chip replaces previous generations (910B/910C) and is designed specifically for training and inference of the largest language models. Chinese tech giants such as ByteDance and Alibaba are already investing billions of dollars in these chips. A major breakthrough is Huawei’s new CANN Next software stack, which emulates NVIDIA’s CUDA environment directly at the hardware level. For Chinese AI labs, this greatly simplifies the transition, as they no longer need to go through the laborious process of rewriting millions of lines of legacy code.

Production Limits and Catching Up to the West

Although development in China is proceeding at an unprecedented pace, hardware there continues to face physical and manufacturing limitations caused by the embargo on Western EUV lithography machines (from the Dutch company ASML). Production of the new Ascend 950PR chip is handled by the domestic foundry SMIC using its more advanced 5nm process (referred to as N+3). However, because it remains dependent on older DUV machines, it struggles with low efficiency and yield. Another difference is the absence of HBM3e/HBM4 memory. Huawei therefore equips the chips with its proprietary memory called HiBL, which is cheaper to manufacture but does not achieve the same extreme bandwidth as Western standards.

From an analytical standpoint, the Ascend 950PR still lags behind the top U.S. chips from NVIDIA and AMD in terms of raw performance. For example, for FP4 computations, the Ascend 950PR delivers around 1.56 PFLOPS, while the NVIDIA B300 achieves more than eight times that. Crucially, however, this hardware is more than sufficient for China’s needs. Thanks to unlimited government funding, the low purchase price of the cards, and massive scaling (using its own high-speed LingQu interconnect network), Huawei can make up for the lack of performance per card simply by connecting thousands of them into giant clusters. For the local market, the Ascend 950 family thus represents a fully self-sufficient and highly functional ecosystem today.