Huawei’s Ascend 950 goes head-to-head with Nvidia’s H200 and AMD’s MI300 in a wild AI horsepower showdown

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Huawei Ascend 950DT FP8 formats target efficient inference without accuracy loss
Nvidia H200 leans on a mature software ecosystem and Hopper GPU strengths
AMD Instinct MI300’s FP64 parity appeals to serious scientific computation workloads

In recent years, the demand for AI training and inference computing has pushed chip makers to innovate aggressively – efficiency in memory bandwidth, data formats, interconnects, and total compute output are now as critical as raw FLOPS.

Each company targets demanding scenarios such as generative AI training and high-performance computing, where AI tools increasingly depend on fast accelerators to process massive datasets.

Multiple brands approach the challenge with different compute platform characteristics – so we’ve tried to help understand these differences and clarify how the Ascend 950 series, H200, and MI300 Instinct compare.

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Huawei Ascend 950 vs Nvidia H200 vs AMD MI300 Instinct
Category	Huawei Ascend 950DT	NVIDIA H200	AMD Radeon Instinct MI300
Chip Family / Name	Ascend 950 series	H200 (GH100, Hopper)	Radeon Instinct MI300 (Aqua Vanjaram)
Architecture	Proprietary Huawei AI accelerator	Hopper GPU architecture	CDNA 3.0
Process / Foundry	Not yet publicly confirmed	5 nm (TSMC)	5 nm (TSMC)
Transistors	Not specified	80 billion	153 billion
Die Size	Not specified	814 mm²	1017 mm²
Optimization	Decode-stage inference & model training	General-purpose AI & HPC acceleration	AI/HPC compute acceleration
Supported Formats	FP8, MXFP8, MXFP4, HiF8	FP16, FP32, FP64 (via Tensor/CUDA cores)	FP16, FP32, FP64
Peak Performance	1 PFLOPS (FP8 / MXFP8 / HiF8), 2 PFLOPS (MXFP4)	FP16: 241.3 TFLOPS, FP32: 60.3 TFLOPS, FP64: 30.2 TFLOPS	FP16: 383 TFLOPS, FP32/FP64: 47.87 TFLOPS
Vector Processing	SIMD + SIMT hybrid, 128-byte memory access granularity	SIMT with CUDA and Tensor cores	SIMT + Matrix/Tensor cores
Memory Type	HiZQ 2.0 proprietary HBM (for decode & training variant)	HBM3e	HBM3
Memory Capacity	144 GB	141 GB	128 GB
Memory Bandwidth	4 TB/s	4.89 TB/s	6.55 TB/s
Memory Bus Width	Not specified	6144-bit	8192-bit
L2 Cache	Not specified	50 MB	Not specified
Interconnect Bandwidth	2 TB/s	Not specified	Not specified
Form Factors	Cards, SuperPoD servers	PCIe 5.0 x16 (server/HPC only)	PCIe 5.0 x16 (compute card)
Base / Boost Clock	Not specified	1365 / 1785 MHz	1000 / 1700 MHz
Cores / Shaders	Not specified	CUDA: 16,896, Tensor: 528 (4th Gen)	14,080 shaders, 220 CUs, 880 Tensor cores
Power (TDP)	Not specified	600 W	600 W
Bus Interface	Not specified	PCIe 5.0 x16	PCIe 5.0 x16
Outputs	None (server use)	None (server/HPC only)	None (compute card)
Target Scenarios	Large-scale training & decode inference (LLMs, generative AI)	AI training, HPC, data centers	AI/HPC compute acceleration
Release / Availability	Q4 2026	Nov 18, 2024	Jan 4, 2023

Architecture and design approaches

Huawei’s Ascend 950 series is a proprietary AI accelerator architecture optimized for the decode stage of inference as well as model training, rather than a traditional GPU.

Its design blends SIMD and SIMT processing styles with 128-byte memory access granularity, aiming to balance throughput and flexibility.

Nvidia’s H200 is based on the Hopper GPU architecture and integrates 16,896 CUDA cores alongside 528 fourth-generation Tensor cores.

It uses a single-die GH100 GPU fabricated on a 5 nm TSMC process, maintaining compatibility with Nvidia’s software stack and extensive ecosystem.

AMD’s MI300 Instinct uses the Aqua Vanjaram GPU with the CDNA 3.0 architecture and a chiplet-based MCM design featuring 220 compute units and 880 matrix cores.

This approach provides a massive transistor budget and a strong focus on high-performance computing.

The Ascend 950 offers peak performance of one petaflop using FP8, MXFP8, or HiF8 data formats and can double to two petaflops when using MXFP4.

This highlights Huawei’s focus on emerging low-precision formats designed to improve efficiency during inference without sacrificing accuracy.

Nvidia’s H200 delivers 241.3 teraflops in FP16 and 60.3 teraflops in FP32, while AMD’s MI300 provides 383 teraflops in FP16 and nearly 48 teraflops for both FP32 and FP64 workloads.

The MI300’s FP64 parity with FP32 underlines its suitability for scientific computation, where double-precision is critical, whereas Nvidia’s focus is skewed toward mixed-precision acceleration for AI.

Memory architecture strongly influences training large language models.

Huawei pairs the Ascend 950 with 144GB of HiZQ 2.0 proprietary HBM, delivering 4TB/s of bandwidth and 2TB/s interconnect speed.

Nvidia equips the H200 with 141GB of HBM3e memory and a 4.89TB/s bandwidth, slightly ahead in raw throughput.

AMD’s MI300 stands out with 128GB of HBM3 but a wider 8192-bit bus and a leading 6.55TB/s memory bandwidth.

For massive model training or memory-intensive simulation, AMD’s advantage in bandwidth can translate into faster data movement even if its total memory capacity trails Huawei’s.

The H200 and MI300 share a 600W thermal design power, fitting into PCIe 5.0 x16 server configurations with no video outputs, underscoring their data center orientation.

Huawei has not disclosed official TDP figures but offers both card formats and integrated SuperPoD servers, suggesting deployment flexibility within its own AI infrastructure solutions.

Its interconnect bandwidth of 2TB/s could be an important factor for multi-chip scaling in data center environments, although details about die size and transistor count remain undisclosed.

Nvidia benefits from a mature NVLink and InfiniBand ecosystem, while AMD’s multi-chip module design aims to reduce latency between compute dies.

Huawei clearly aims its Ascend 950 at large-scale training and decode-stage inference for generative AI, a market where Nvidia has long dominated.

Its Q4 2026 availability means Nvidia’s H200, released in late 2024, and AMD’s MI300, available since early 2023, already have a time advantage.

By the time Ascend 950 hardware reaches customers, both competitors may have iterated on their platforms.

However, Huawei’s emphasis on efficient low-precision formats and tight integration with its networking hardware could attract buyers seeking alternatives to U.S. suppliers.

That said, these accelerators reflect differing philosophies of multiple brands.

AMD prioritizes memory bandwidth and double-precision strength for HPC workloads, while Nvidia leverages ecosystem maturity and software support to maintain dominance in AI training.

Huawei seeks to challenge both with aggressive FP8-class performance and high-capacity proprietary memory.

Via Huawei, Nvidia, TechPowerUp

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