AMD Instinct MI308X vs NVIDIA H100 CNX Comparison

Head-to-Head Benchmarks

The recorded data shows no direct head-to-head benchmark results for the AMD Instinct MI308X versus the NVIDIA H100 CNX. Consequently, there are no wins recorded for either product in direct comparison. Both units occupy the same 50th percentile versus all GPUs in the database, with an average benchmark score of 0 for each. This indicates that the database currently holds no empirical performance measurements for either accelerator in isolation or in direct competition.

Without benchmark scores, the analysis must rely on architectural and specification-level comparisons derived from the recorded data. The AMD Instinct MI308X lists a boost clock of 2100 MHz, while the NVIDIA H100 CNX lists a boost clock of 1845 MHz. In FP32 throughput, the MI308X records 81.72 TFLOPS, which stands 51.8% above the H100 CNX's 53.84 TFLOPS. Conversely, in FP16 throughput, the H100 CNX records 215.4 TFLOPS (4:1 ratio), which is 163.6% above the MI308X's 81.72 TFLOPS (1:1 ratio). These figures represent the clearest numeric deltas between the two, though they are specification-derived rather than benchmark-derived.

The MI308X also lists a texture rate of 2,553.6 GTexel/s, which is 203.6% higher than the H100 CNX's 841.3 GTexel/s. The H100 CNX lists a pixel rate of 44.28 GPixel/s, while the MI308X lists 0 MPixel/s, indicating the MI308X has no raster operation pipeline in the recorded data. Memory bandwidth favors the MI308X at 5.32 TB/s versus the H100 CNX's 2.04 TB/s, a 160.8% advantage. The MI308X has 192 GB of HBM3 memory, while the H100 CNX has 80 GB of HBM2e, a 140% capacity difference. These are the primary numeric differentiators in the absence of benchmark outcomes.

Where Each One Wins

Based on specification-level data, the AMD Instinct MI308X demonstrates advantages in FP32 compute, texture throughput, memory capacity, memory bandwidth, and clock speeds. The FP32 figure of 81.72 TFLOPS suggests suitability for workloads that rely on single-precision arithmetic, such as certain scientific simulations and general-purpose compute tasks. The texture rate of 2,553.6 GTexel/s indicates strong texel processing capability, which may benefit workloads that involve dense texture sampling or similar memory-access patterns. The memory subsystem, with 192 GB at 5.32 TB/s, provides a larger and faster data path, which could reduce bottlenecks in data-intensive operations like large model inference or training datasets that exceed the H100 CNX's 80 GB capacity.

The NVIDIA H100 CNX shows advantages in FP16 throughput, pixel rate, and power efficiency as indicated by its lower TDP. The FP16 figure of 215.4 TFLOPS (4:1) is substantially higher than the MI308X's 81.72 TFLOPS (1:1), suggesting that the H100 CNX is optimized for mixed-precision workloads such as deep learning training where FP16 is the dominant precision. The pixel rate of 44.28 GPixel/s, while modest, is present where the MI308X has none, indicating some rasterization capability exists on the H100 CNX. The TDP of 350 W versus 750 W means the H100 CNX draws less power under load, which may be relevant for dense server deployments where thermal and power budgets are constrained.

The data also shows the H100 CNX has a dual-slot form factor with an 8-pin EPS power connector, while the MI308X is an OAM module with no power connectors listed. This distinction affects physical integration: the H100 CNX fits into standard PCIe 5.0 x16 server slots, whereas the MI308X requires an OAM baseboard. The H100 CNX is listed as active in production, while the MI308X has no production status recorded. Neither product has a launch MSRP in the database.

Architecture Differences

The AMD Instinct MI308X uses the Aqua Vanjaram chip built on CDNA 3.0 architecture, fabricated on a 5 nm process at TSMC. The chip contains 153,000 million transistors on a die size of 1017 mm², yielding a transistor density of 150.4 million per mm². The NVIDIA H100 CNX uses the GH100 chip built on Hopper architecture, also on a 5 nm TSMC process, with 80,000 million transistors on a 814 mm² die, giving a density of 98.3 million per mm². The MI308X has nearly double the transistor count and a 24.9% larger die.

The MI308X lists 19,456 shading units and 1,216 texture mapping units, with no raster operations units recorded. The H100 CNX lists 14,592 shading units, 456 texture mapping units, and 24 raster operations units. The H100 CNX also lists 456 tensor cores, while the MI308X has no tensor core count recorded. The MI308X has no pixel rate, while the H100 CNX has a pixel rate of 44.28 GPixel/s.

Memory architecture differs significantly. The MI308X uses 192 GB of HBM3 with an 8192-bit bus width, achieving 5.32 TB/s bandwidth. The H100 CNX uses 80 GB of HBM2e with a 5120-bit bus, achieving 2.04 TB/s bandwidth. Clock speeds also diverge: the MI308X has a base of 1000 MHz and boost of 2100 MHz, while the H100 CNX has a base of 690 MHz and boost of 1845 MHz. Memory clocks are listed as 1300 MHz (5.2 Gbps effective) for the MI308X and 1593 MHz (3.2 Gbps effective) for the H100 CNX.

Power and physical specifications differ: the MI308X has a TDP of 750 W with a suggested PSU of 1150 W, while the H100 CNX has a TDP of 350 W with a suggested PSU of 750 W. The MI308X is an OAM module with no power connectors, while the H100 CNX is a dual-slot card measuring 267 mm by 111 mm, using an 8-pin EPS connector. Both use PCIe 5.0 x16 interfaces and have no display outputs. The MI308X lists no API support (DirectX, OpenGL, Vulkan all N/A), while the H100 CNX has null entries for these APIs. The MI308X was released on 2023-12-05, succeeding Radeon Instinct, while the H100 CNX was released on 2023-03-20, succeeding Server Ada and preceding Server Blackwell.

FAQ

Q: Which product has higher FP32 compute throughput?

A: The AMD Instinct MI308X records 81.72 TFLOPS FP32, which is 51.8% above the NVIDIA H100 CNX's 53.84 TFLOPS.

Q: Which product has higher FP16 throughput?

A: The NVIDIA H100 CNX records 215.4 TFLOPS FP16 (4:1 ratio), which is 163.6% above the AMD Instinct MI308X's 81.72 TFLOPS (1:1 ratio).

Q: What are the memory capacity and bandwidth differences?

A: The MI308X has 192 GB of HBM3 with 5.32 TB/s bandwidth, while the H100 CNX has 80 GB of HBM2e with 2.04 TB/s bandwidth. The MI308X has 140% more capacity and 160.8% more bandwidth.

Q: How do the physical dimensions and power requirements compare?

A: The MI308X is an OAM module with a 750 W TDP and a suggested PSU of 1150 W, while the H100 CNX is a dual-slot card measuring 267 mm by 111 mm with a 350 W TDP and a suggested PSU of 750 W, using an 8-pin EPS connector.

Q: Which product has tensor cores?

A: The NVIDIA H100 CNX lists 456 tensor cores, while the AMD Instinct MI308X has no tensor core count recorded in the database.

Q: What are the release dates?

A: The H100 CNX was released on 2023-03-20, and the MI308X was released on 2023-12-05.

The Verdict

The data indicates that the AMD Instinct MI308X is positioned for workloads that prioritize FP32 compute, memory capacity, and memory bandwidth. Its 81.72 TFLOPS FP32, 192 GB HBM3, and 5.32 TB/s bandwidth exceed the H100 CNX on these metrics. The absence of raster operations and tensor cores, along with zero pixel rate, suggests a compute-focused accelerator without graphics or mixed-precision tensor processing paths in the recorded specifications.

The NVIDIA H100 CNX is positioned for workloads that prioritize FP16 throughput and power efficiency. Its 215.4 TFLOPS FP16 (4:1), 456 tensor cores, and 350 W TDP indicate a design optimized for deep learning training and inference where FP16 is standard. The dual-slot form factor and 8-pin EPS connector allow standard server installation, while the active production status indicates ongoing availability.

For users with FP32-dominated workloads and datasets exceeding 80 GB, the MI308X provides larger memory and higher bandwidth. For users with FP16-dominated workloads and constrained power budgets, the H100 CNX provides higher mixed-precision throughput at less than half the TDP. Neither product has benchmark scores or a launch MSRP in the database, so direct performance validation remains unavailable. The choice between them depends on whether the workload aligns with the MI308X's FP32 and capacity advantages or the H100 CNX's FP16 and efficiency advantages.