AMD Instinct MI100 vs NVIDIA H200 NVL Comparison

# NVIDIA H200 NVL vs AMD Instinct MI100

The NVIDIA H200 NVL and AMD Instinct MI100 represent two very different eras of server accelerator design. The H200 NVL, built on Hopper architecture and released in late 2024, delivers a Geekbench OpenCL score of 334,891, placing it in the 100th percentile of all GPUs. The MI100, a CDNA 1.0 part from late 2020, scores 139,035 and sits in the 96th percentile. The gap between them is enormous — the H200 NVL leads by 140.9% in the sole head-to-head benchmark — but the MI100 remains a relevant comparison point for understanding generational scaling, memory configuration trade-offs, and workload-specific strengths that are not captured by a single compute score.

Where Each One Wins

The NVIDIA H200 NVL wins the only benchmark recorded in this comparison: Geekbench OpenCL, with a score of 334,891 versus 139,035 for the AMD Instinct MI100. That is a 140.9% advantage, meaning the H200 NVL more than doubles the MI100's raw compute throughput in this test. The H200 NVL's wins extend to essentially every measurable compute category: it has more than double the FP32 throughput (60.32 TFLOPS vs 23.07 TFLOPS), more than double the FP16 throughput (120.6 TFLOPS vs 46.14 TFLOPS), and substantially higher texture fill rate (942.5 GTexel/s vs 721.0 GTexel/s). The H200 NVL also leads in memory bandwidth by a factor of nearly four (4.89 TB/s vs 1.23 TB/s), which is critical for large-model inference and training workloads that are bandwidth-bound.

The AMD Instinct MI100, however, retains some specific advantages. Its pixel rate of 96.13 GPixel/s is more than double the H200 NVL's 42.84 GPixel/s, suggesting that the MI100 has a higher ROP count (64 vs 24) and may be better suited for certain rasterization-style workloads, even though neither card has display outputs. The MI100 also draws only 300 W TDP versus 600 W for the H200 NVL, making it a lower-power option for systems where thermal or power delivery constraints are primary. Its transistor density is lower (34.1M / mm² vs 98.3M / mm²), which reflects the older 7 nm process node, but the MI100's die size of 750 mm² is only slightly smaller than the H200 NVL's 814 mm², indicating that the H200 NVL packs far more transistors (80,000 million vs 25,600 million) into a marginally larger package.

FAQ

Q: Which GPU has a higher Geekbench OpenCL score?

A: The NVIDIA H200 NVL scores 334,891, which is 140.9% higher than the AMD Instinct MI100's 139,035. The H200 NVL ranks in the 100th percentile of all GPUs, while the MI100 ranks in the 96th percentile.

Q: How does the memory configuration differ between these two accelerators?

A: The H200 NVL has 141 GB of HBM3e memory on a 6144-bit bus, delivering 4.89 TB/s of bandwidth. The MI100 has 32 GB of HBM2 memory on a 4096-bit bus, providing 1.23 TB/s. The H200 NVL offers more than four times the capacity and four times the bandwidth.

Q: What is the performance gap relative to their closest rivals?

A: The H200 NVL is 3.1% behind the NVIDIA B200 (345,482) and 5.3% ahead of the AMD Instinct MI300X (317,994). The MI100 is 0.7% ahead of the NVIDIA Tesla V100 PCIe 16 GB (138,063) and 0.9% ahead of the Tesla V100 SXM2 32 GB (137,731).

Q: Which card has higher compute throughput in FP32 and FP16?

A: The H200 NVL delivers 60.32 TFLOPS FP32 and 120.6 TFLOPS FP16, compared to 23.07 TFLOPS FP32 and 46.14 TFLOPS FP16 for the MI100. The H200 NVL is roughly 2.6 times faster in both precisions.

Q: What are the power requirements for each card?

A: The H200 NVL has a 600 W TDP and requires a 1000 W suggested PSU with a single 8-pin EPS connector. The MI100 has a 300 W TDP, requires a 700 W suggested PSU, and uses two 8-pin power connectors.

Q: Which card is still in production?

A: The NVIDIA H200 NVL has an active production status, while the AMD Instinct MI100 is marked as end-of-life. The H200 NVL was released on 2024-11-17, and the MI100 was released on 2020-11-15.

Head-to-Head Benchmarks

The only direct benchmark available is Geekbench OpenCL, where the NVIDIA H200 NVL achieves 334,891 points against 139,035 for the AMD Instinct MI100. The delta of 140.9% means the H200 NVL is 2.4 times faster in this test. This is not a marginal victory; it is a generational leap. The H200 NVL's 100th percentile ranking means it outperforms 100% of all GPUs in the database, while the MI100's 96th percentile places it in the upper tier but clearly below the current flagship class.

Interpreting the score differential requires context from the nearest rivals. The H200 NVL sits within 3.1% of the NVIDIA B200 (345,482) and 9.4% below the NVIDIA B300 SXM6 AC (369,831), placing it in the top echelon of current accelerators. It also leads the AMD Instinct MI300X by 5.3%, showing that AMD's newer parts close but do not eliminate the gap. The MI100, by contrast, is nearly tied with the Tesla V100 variants, leading the V100 PCIe 16 GB by only 0.7% and the V100 SXM2 32 GB by 0.9%. It also edges out the AMD Radeon PRO V620 by 1.9% and the Radeon Pro W6800X Duo by 2.4%. This tells a clear story: the MI100 is competitive with the previous generation's flagship, but the H200 NVL operates in a different performance class entirely.

The texture rate difference reinforces the compute gap. The H200 NVL's 942.5 GTexel/s is 30.7% higher than the MI100's 721.0 GTexel/s, which matters for workloads that sample textures or structured grids. The H200 NVL also has significantly more shading units (16,896 vs 7,680) and TMUs (528 vs 480), though the TMU count is closer than the shading unit count might suggest. The MI100's higher pixel rate (96.13 GPixel/s vs 42.84 GPixel/s) is the one metric where the older card wins, but this is unlikely to matter in typical server workloads given that both cards have no display outputs and are designed for compute acceleration.

Specification Differences

The memory subsystem is the most dramatic differentiator. The H200 NVL uses 141 GB of HBM3e with a 6144-bit bus and 4.89 TB/s bandwidth, while the MI100 uses 32 GB of HBM2 with a 4096-bit bus and 1.23 TB/s bandwidth. The H200 NVL's memory clock is 1593 MHz (6.4 Gbps effective) versus 1200 MHz (2.4 Gbps effective) for the MI100. This combination of higher capacity, wider bus, and faster memory gives the H200 NVL a 297% bandwidth advantage.

Compute resources differ substantially. The H200 NVL has 16,896 shading units, 528 TMUs, 24 ROPs, and 528 tensor cores. The MI100 has 7,680 shading units, 480 TMUs, 64 ROPs, and no tensor cores. The H200 NVL's FP32 throughput is 60.32 TFLOPS versus 23.07 TFLOPS, and FP16 is 120.6 TFLOPS versus 46.14 TFLOPS. Clock speeds also favor the newer card: the H200 NVL boosts to 1785 MHz from a 1365 MHz base, while the MI100 boosts to 1502 MHz from a 1000 MHz base.

Power and interface specs reveal the generational shift. The H200 NVL has a 600 W TDP with a 1000 W suggested PSU and a single 8-pin EPS connector. The MI100 has a 300 W TDP with a 700 W suggested PSU and two 8-pin connectors. The H200 NVL uses PCIe 5.0 x16, while the MI100 uses PCIe 4.0 x16. Both are dual-slot cards with identical physical dimensions (267 mm length, 111 mm height) and no display outputs. The H200 NVL is built on a 5 nm process at TSMC with 80,000 million transistors on an 814 mm² die (98.3M / mm² density). The MI100 uses TSMC's 7 nm process with 25,600 million transistors on a 750 mm² die (34.1M / mm² density).

Architecture Differences

The H200 NVL is based on the GH100 chip using Hopper architecture, part of the Server Hopper (Hxx) generation. Hopper is NVIDIA's dedicated data-center compute architecture, designed with a focus on transformer models, large-scale AI training, and high-bandwidth memory integration. The presence of 528 tensor cores is a defining feature — these are specialized matrix-math units that accelerate deep learning operations. Hopper also introduces features like the 5 nm process, which enables the extremely high transistor density of 98.3M / mm².

The MI100 uses the Arcturus chip with CDNA 1.0 architecture, part of the Instinct (MIx) generation. CDNA 1.0 is AMD's first dedicated compute architecture, separating from the RDNA gaming line to focus on server workloads. It has no tensor cores, relying instead on traditional shader-based compute. The 7 nm process allows 34.1M / mm² transistor density, which is less than half the H200 NVL's density. The MI100's architecture is designed for FP32 and FP16 compute, with the 2:1 FP16 ratio indicating that FP16 throughput is exactly double FP32 — the same ratio as the H200 NVL.

The memory architectures reflect different design priorities. The H200 NVL's HBM3e with 141 GB capacity targets large language models and datasets that must reside in GPU memory to avoid PCIe transfers. The MI100's 32 GB HBM2 is adequate for the workloads of its era but is now a limiting factor for modern AI models. The H200 NVL's 4.89 TB/s bandwidth is essential for feeding its 16,896 shading units, while the MI100's 1.23 TB/s bandwidth matches its 7,680 shading units.

The H200 NVL's predecessor is Server Ada, and its successor is Server Blackwell, indicating it sits in the middle of NVIDIA's recent server GPU roadmap. The MI100's predecessor is Radeon Instinct, and it has no listed successor, reflecting AMD's transition to newer CDNA generations. The H200 NVL was released on 2024-11-17 and remains active in production, while the MI100 was released on 2020-11-15 and is now end-of-life. These release dates explain the architectural gap: the H200 NVL benefits from four years of process and design improvements, including a smaller node, higher transistor count, and more specialized compute units.

Both cards are compute-focused with no display outputs and no DirectX, OpenGL, or Vulkan API support, confirming they are intended exclusively for server and data-center deployments. The H200 NVL's PCIe 5.0 interface doubles the MI100's PCIe 4.0 bandwidth, which matters for systems that move data between GPUs or to host memory. The power difference — 600 W versus 300 W — is substantial, but the H200 NVL delivers more than double the compute performance per watt in FP32, making the higher power draw a reasonable trade-off for performance-oriented deployments.