AMD Instinct MI350P vs NVIDIA H20 Comparison

FAQ

Q: What are the architectural generations of these two accelerators?

A: The AMD Instinct MI350P uses the CDNA 4.0 architecture built on a 3 nm process, while the NVIDIA H20 is based on the Hopper architecture on a 5 nm process. Both are manufactured by TSMC.

Q: How do their memory subsystems compare?

A: The MI350P has 144 GB of HBM3e on an 8192-bit bus with 8.19 TB/s bandwidth. The H20 has 96 GB of HBM3 on a 6144-bit bus with 4.03 TB/s bandwidth. The MI350P doubles the bandwidth of the H20.

Q: Which device has higher boost clocks?

A: The NVIDIA H20 boosts to 1980 MHz, which is lower than the MI350P's boost clock of 2200 MHz. However, the H20 has a significantly higher base clock at 1830 MHz versus 1000 MHz for the MI350P.

Q: What is the transistor count difference?

A: The NVIDIA H20 contains 80,000 million transistors on an 814 mm² die, while the AMD MI350P has 73,000 million transistors on a larger 1190 mm² die. The H20 achieves a higher transistor density of 98.3M per mm² versus 61.3M per mm² for the MI350P.

Q: Do these cards support display outputs?

A: Neither card has display outputs. Both are compute-focused accelerators with no video connectivity.

Q: What are the power requirements?

A: The MI350P has a 600 W TDP and suggests a 1000 W power supply, while the H20 has a 500 W TDP and suggests a 900 W power supply. The MI350P uses a single 16-pin power connector; the H20's connector details are not recorded.

Architecture Differences

The AMD Instinct MI350P and NVIDIA H20 diverge sharply in their design philosophies. The MI350P is built on CDNA 4.0, AMD's dedicated compute architecture, manufactured on a 3 nm TSMC process. The H20 belongs to NVIDIA's Hopper generation, fabricated on a 5 nm TSMC node. The process difference helps explain why the MI350P packs 73,000 million transistors onto a 1190 mm² die, while the H20 fits 80,000 million transistors into 814 mm². The H20's density advantage, 98.3M transistors per mm² versus 61.3M per mm², suggests a more compact layout despite the older node.

Clock behavior differs substantially. The MI350P has a 1000 MHz base clock that boosts to 2200 MHz, a wide dynamic range that allows it to ramp aggressively under load. The H20 runs at 1830 MHz base and 1980 MHz boost, a much tighter range with a lower ceiling. The MI350P's boost advantage of 220 MHz over the H20's maximum clock is notable, but the H20's higher base clock indicates it sustains performance without needing to scale up as far.

Memory architecture is a defining difference. The MI350P uses HBM3e with 144 GB capacity and an 8192-bit bus, yielding 8.19 TB/s bandwidth. The H20 uses HBM3 with 96 GB capacity and a 6144-bit bus, yielding 4.03 TB/s. The MI350P delivers just over double the memory bandwidth, which is critical for large model inference and training workloads that are memory-bound.

The compute resources are organized differently. The MI350P has 8192 shading units, 512 texture mapping units, and no ROPs, which is typical for a compute card that skips rasterization hardware. It has no dedicated tensor cores listed; its FP16 throughput is identical to FP32 at 36.04 TFLOPS, indicating a 1:1 ratio. The H20, by contrast, has 9984 shading units, 312 TMUs, 24 ROPs, and 312 tensor cores. Its FP16 throughput reaches 79.07 TFLOPS at a 2:1 ratio, meaning it processes half-precision at double the rate of FP32. The H20's FP32 rate of 39.54 TFLOPS slightly exceeds the MI350P's 36.04 TFLOPS.

Physical design also differs. The MI350P is a dual-slot card measuring 267 mm in length, 111 mm in height, and 40 mm in width, with a 16-pin power connector. The H20 is an SXM module, a board form factor without recorded dimensions or a standard power connector. The MI350P is a PCIe 5.0 x16 card; the H20 also uses PCIe 5.0 x16. Both lack display outputs and have no DirectX, OpenGL, or Vulkan API support, confirming they are server compute parts rather than graphics cards.

Head-to-Head Benchmarks

The recorded benchmark data contains no direct head-to-head results, and both accelerators show an average benchmark score of zero with no nearest rivals listed. The percentile ranking for each is 50th among all GPUs, which places them at the median in the database's distribution, but the absence of scored workloads means the comparison must rely on architectural specifications and derived capabilities.

The clearest win for the MI350P is memory bandwidth. At 8.19 TB/s, it is roughly 103% higher than the H20's 4.03 TB/s. For workloads that stream large matrices or model weights, this advantage is substantial. The MI350P also leads in memory capacity with 144 GB versus 96 GB, a 50% increase. Texture fill rate favors the MI350P at 1,126.4 GTexel/s versus 617.8 GTexel/s for the H20, nearly doubling the H20's rate.

The H20 counters in raw FP32 throughput. Its 39.54 TFLOPS is about 9.7% higher than the MI350P's 36.04 TFLOPS. The H20's FP16 output of 79.07 TFLOPS is more than double the MI350P's 36.04 TFLOPS, reflecting the tensor core advantage and the 2:1 FP16 ratio. The H20 also has a higher pixel rate at 47.52 GPixel/s, though the MI350P records zero pixel rate due to its lack of ROPs. The H20's higher base clock of 1830 MHz versus 1000 MHz suggests it maintains a higher floor of sustained throughput in lightly threaded or fixed-frequency scenarios.

The MI350P's boost clock of 2200 MHz is 11.1% higher than the H20's 1980 MHz boost. The MI350P also has more TMUs, 512 versus 312, which aligns with its higher texture rate. The H20 has more shading units, 9984 versus 8192, which explains its FP32 lead despite the lower boost clock.

Power efficiency is not directly recorded as a metric, but the TDP figures are in the data. The MI350P draws 600 W to achieve its bandwidth and FP16-equivalent throughput, while the H20 draws 500 W for its FP32 and FP16 output. The H20 delivers higher FP32 per watt and much higher FP16 per watt based on these figures. The MI350P's bandwidth per watt is higher, at roughly 13.65 GB/s per watt versus 8.06 GB/s per watt for the H20.

Specification Differences

The two accelerators differ across nearly every major specification category. The MI350P uses a 3 nm process; the H20 uses 5 nm. Transistor counts are 73,000 million for the MI350P and 80,000 million for the H20. Die sizes are 1190 mm² and 814 mm² respectively, giving transistor densities of 61.3M per mm² and 98.3M per mm².

Clock speeds differ in both base and boost. The MI350P runs at 1000 MHz base and 2200 MHz boost. The H20 runs at 1830 MHz base and 1980 MHz boost. Memory clocks also differ: the MI350P uses 2000 MHz with 8 Gbps effective data rate, while the H20 uses 1313 MHz with 5.3 Gbps effective.

Memory capacity is 144 GB HBM3e for the MI350P versus 96 GB HBM3 for the H20. Bus widths are 8192 bit and 6144 bit, and bandwidths are 8.19 TB/s and 4.03 TB/s. Shading units count 8192 for the MI350P and 9984 for the H20. Texture mapping units are 512 versus 312. The MI350P has zero ROPs; the H20 has 24. The H20 has 312 tensor cores; the MI350P lists none.

Pixel rates are 0 MPixel/s for the MI350P and 47.52 GPixel/s for the H20. Texture rates are 1,126.4 GTexel/s and 617.8 GTexel/s. FP32 performance is 36.04 TFLOPS for the MI350P and 39.54 TFLOPS for the H20. FP16 performance is 36.04 TFLOPS (1:1) for the MI350P and 79.07 TFLOPS (2:1) for the H20.

TDP is 600 W for the MI350P and 500 W for the H20. The suggested power supply is 1000 W for the MI350P and 900 W for the H20. The MI350P is dual-slot with a 16-pin connector; the H20 is an SXM module with no connector listed. Dimensions are recorded only for the MI350P at 267 mm by 111 mm by 40 mm. The H20 has no recorded dimensions.

Release dates differ as well. The MI350P is dated May 6, 2026, while the H20 is dated January 31, 2024. The H20 has an active production status; the MI350P's status is not recorded. The H20 lists a successor in Server Blackwell; the MI350P has no successor listed.

Where Each One Wins

The MI350P wins decisively in memory-centric workloads. Its 8.19 TB/s bandwidth and 144 GB capacity make it suited for large language model inference, massive embedding tables, or any workload where the model exceeds 96 GB or where bandwidth saturation is the limiting factor. The texture rate of 1,126.4 GTexel/s also suggests strength in compute patterns that map to texture-like gather operations, though this is secondary for typical AI workloads.

The H20 wins in arithmetic throughput, especially mixed-precision. Its 79.07 TFLOPS FP16 output, enabled by 312 tensor cores, gives it a strong edge for training and inference that uses half-precision tensors. Its FP32 rate of 39.54 TFLOPS also exceeds the MI350P's 36.04 TFLOPS, making it the better choice for FP32-heavy scientific computing where the extra 9.7% matters. The H20's higher base clock of 1830 MHz ensures consistent performance without relying on boost scaling.

The MI350P's higher boost clock of 2200 MHz, which is 11.1% above the H20's 1980 MHz, may help in bursty workloads where short-duration compute peaks are common. The H20's lower TDP of 500 W and suggested 900 W power supply, versus 600 W and 1000 W for the MI350P, makes it easier to integrate into existing server infrastructure with lower power overhead.

The MI350P's dual-slot PCIe form factor is more flexible for standard server chassis, while the H20's SXM module requires a compatible baseboard. The MI350P's larger memory capacity and bandwidth position it for memory-bound applications, while the H20's tensor cores and higher FP16 throughput position it for compute-bound mixed-precision workloads.

The Verdict

The AMD Instinct MI350P is the choice for workloads that demand maximum memory bandwidth and capacity. Its 8.19 TB/s bandwidth, more than double the H20's 4.03 TB/s, and 144 GB capacity give it a clear advantage in large-scale inference, recommendation systems, and other memory-bound models that cannot fit in 96 GB. The data shows the MI350P also leads in texture rate and boost clock, which may translate to advantages in certain compute patterns.

The NVIDIA H20 is the choice for FP16-heavy compute. Its 79.07 TFLOPS FP16 throughput, delivered by 312 tensor cores, is more than double the MI350P's 36.04 TFLOPS. The H20 also leads in FP32 at 39.54 TFLOPS and has a higher base clock of 1830 MHz, making it the more balanced option for general compute that does not require extreme memory bandwidth. Its lower TDP of 500 W and active production status add operational appeal.

The recorded benchmark data shows no scored wins for either card, and both sit at the 50th percentile in the database. The absence of direct measurements means the verdict rests on specification analysis. For memory-bound AI workloads, the MI350P's doubled bandwidth and 50% larger memory pool are decisive. For mixed-precision training and FP32 scientific workloads, the H20's tensor cores and higher arithmetic rates give it the edge. The choice depends on whether the bottleneck is memory movement or computation, and the specification data clearly splits along those lines.