AMD Instinct MI350P vs AMD Radeon Instinct MI300 Comparison

Head-to-Head Benchmarks

The database contains no recorded benchmark scores for either the AMD Instinct MI350P or the AMD Radeon Instinct MI300. Both cards show an average benchmark score of zero, and the head-to-head benchmark comparison table is empty. Consequently, there are no measured wins for either accelerator across any workload category. The percentile versus all GPUs is identical for both at the 50th mark, a neutral placement that reflects the absence of test data rather than a meaningful performance equivalence.

Without direct measurements, the only quantitative performance indicators available are the theoretical compute specifications. The MI300 posts a higher FP32 throughput at 47.87 TFLOPS, while the MI350P delivers 36.04 TFLOPS. In FP16 work, the MI300 reaches 383.0 TFLOPS using an 8:1 ratio, whereas the MI350P offers 36.04 TFLOPS at a 1:1 ratio. These are architectural throughput ceilings, not application results, and the database does not indicate how either part behaves under real workloads.

Texture rate also favors the MI300. The older card achieves 1,496.0 GTexel/s against 1,126.4 GTexel/s for the MI350P. Pixel rate is zero for both, as neither accelerator has raster output units. Both cards are compute-oriented accelerators with no display outputs, so the absence of pixel processing is expected.

The clear takeaway from the recorded data is that no benchmark conclusions can be drawn. Any comparison must rely on specification analysis, and the database offers no evidence that one part outperforms the other in tested scenarios.

FAQ

Q: Which accelerator has the higher FP32 compute throughput?

A: The AMD Radeon Instinct MI300 shows 47.87 TFLOPS FP32, while the AMD Instinct MI350P shows 36.04 TFLOPS FP32. The MI300 holds a 11.83 TFLOPS advantage in single-precision peak throughput.

Q: How does memory bandwidth compare between the two cards?

A: The MI350P uses 144 GB of HBM3e with 8.19 TB/s bandwidth, while the MI300 uses 128 GB of HBM3 with 6.55 TB/s bandwidth. The MI350P provides 1.64 TB/s more bandwidth and 16 GB more capacity.

Q: What is the transistor count difference?

A: The MI300 packs 153,000 million transistors, while the MI350P has 73,000 million. The MI300 has more than double the transistor count despite a smaller process node.

Q: Do both cards use the same power connector configuration?

A: No. The MI350P uses a single 16-pin connector, whereas the MI300 uses two 8-pin connectors. Both have a 600 W TDP and a suggested PSU of 1000 W.

Q: Are both accelerators the same physical size?

A: Both cards share identical length and height at 267 mm and 111 mm respectively. The MI350P has a width of 40 mm, while the MI300's width is not recorded in the database.

Q: Which architecture is newer?

A: The MI350P uses CDNA 4.0 on a 3 nm process, while the MI300 uses CDNA 3.0 on a 5 nm process. The MI350P is the newer architecture.

Where Each One Wins

Based on recorded specifications, the MI300 wins in raw compute throughput. Its FP32 figure of 47.87 TFLOPS exceeds the MI350P's 36.04 TFLOPS, and its FP16 throughput of 383.0 TFLOPS dwarfs the MI350P's 36.04 TFLOPS. Texture rate also favors the MI300 at 1,496.0 GTexel/s versus 1,126.4 GTexel/s. For workloads that scale with peak arithmetic throughput, such as dense matrix operations or high-throughput inference with FP16 precision, the MI300's numbers indicate a substantial ceiling advantage. Its 8:1 FP16 ratio suggests the architecture prioritizes mixed-precision throughput over full-rate FP16 compute.

The MI350P wins on memory capacity and bandwidth. With 144 GB against 128 GB, the newer card offers 16 GB more on-board memory. Bandwidth rises from 6.55 TB/s to 8.19 TB/s, a 25% improvement that directly benefits memory-bound workloads. Its 8 Gbps effective memory clock versus 6.4 Gbps effective on the MI300 further supports this advantage. For large language models, massive embedding tables, or datasets that exceed 128 GB, the MI350P's larger memory pool and faster HBM3e interface provide a tangible edge. The newer 3 nm process also suggests lower power density per transistor, though the TDP remains identical at 600 W.

The MI350P also wins on transistor efficiency. While the MI300 has 153,000 million transistors, the MI350P achieves its performance with 73,000 million, a reduction of 80,000 million. The MI350P's die size of 1190 mm² is larger than the MI300's 1017 mm², but its transistor density of 61.3M per mm² is lower than the MI300's 150.4M per mm². This indicates the MI350P uses fewer transistors spread over a larger die, potentially improving thermal behavior per transistor under sustained load.

Specification Differences

The two accelerators diverge across nearly every major specification category. The MI350P uses a 3 nm process node, while the MI300 uses 5 nm. Both are fabricated by TSMC. Transistor counts differ significantly: 73,000 million for the MI350P versus 153,000 million for the MI300. Die size is 1190 mm² for the MI350P against 1017 mm² for the MI300.

Memory differs in both capacity and type. The MI350P carries 144 GB of HBM3e with 8.19 TB/s bandwidth and a 2000 MHz memory clock (8 Gbps effective). The MI300 carries 128 GB of HBM3 with 6.55 TB/s bandwidth and a 1600 MHz memory clock (6.4 Gbps effective). Both use an 8192-bit bus width.

Compute unit counts diverge substantially. The MI350P has 8192 shading units, 512 texture mapping units, and 0 raster output units. The MI300 has 14080 shading units, 880 texture mapping units, and 0 raster output units. Clock speeds also differ: the MI350P boosts to 2200 MHz, while the MI300 boosts to 1700 MHz. Base clocks are identical at 1000 MHz.

Power delivery differs. The MI350P uses a single 16-pin connector, while the MI300 uses two 8-pin connectors. Both have a 600 W TDP and a suggested PSU of 1000 W. The MI350P is dual-slot, while the MI300's slot width is not recorded. Physical dimensions match at 267 mm length and 111 mm height, with the MI350P having a 40 mm width and the MI300's width unrecorded.

The MI350P lists no API support for DirectX, OpenGL, or Vulkan, while the MI300's API fields are null. Both cards have no display outputs and use PCIe 5.0 x16 interfaces. Release dates differ, with the MI350P dated 2026-05-06 and the MI300 dated 2023-01-03.

Architecture Differences

The MI350P is built on CDNA 4.0, the fourth generation of AMD's compute-optimized architecture. The MI300 uses CDNA 3.0. This generation gap explains several specification changes. The MI350P's 3 nm process node replaces the MI300's 5 nm node, and the newer node enables a higher boost clock of 2200 MHz against 1700 MHz despite the same 600 W TDP.

The chip codenames differ: the MI350P uses the MI350 128CU chip, while the MI300 uses Aqua Vanjaram. The MI350P's 128 compute units align with its 8192 shading units, while the MI300's 14080 shading units indicate a larger compute-unit configuration. The MI300's 8:1 FP16 ratio versus the MI350P's 1:1 ratio represents a fundamental architectural difference in mixed-precision handling. The MI350P dedicates full rate to FP16, while the MI300 uses a packed path that boosts FP16 throughput to 383.0 TFLOPS but likely with different precision characteristics.

Memory architecture advances with the MI350P. The shift from HBM3 to HBM3e increases effective memory clock from 6.4 Gbps to 8 Gbps, raising bandwidth from 6.55 TB/s to 8.19 TB/s on the same 8192-bit bus. The MI350P also increases capacity from 128 GB to 144 GB. The MI300's transistor density of 150.4M per mm² is far higher than the MI350P's 61.3M per mm², which suggests the MI350P uses a less dense design with larger, more efficient cells or a different transistor mix.

The MI350P's release date of 2026-05-06 places it after the MI300's 2023-01-03 release. The MI350P's predecessor is listed as Radeon Instinct, while the MI300's predecessor is FirePro Data Center. Both have no successor recorded.

The Verdict

The database provides no benchmark scores for either accelerator, so the verdict must rest on specification analysis alone. The MI300 is the arithmetic powerhouse. Its FP32 peak of 47.87 TFLOPS and FP16 peak of 383.0 TFLOPS are substantially higher than the MI350P's 36.04 TFLOPS on both fronts. Texture rate also favors the MI300. Users running workloads that saturate peak compute, particularly FP16-heavy AI training with high batch sizes, should favor the MI300 based on these numbers.

The MI350P is the memory and efficiency choice. Its 144 GB capacity, 8.19 TB/s bandwidth, and faster 2200 MHz boost clock make it well suited for memory-bound inference, large embedding models, or datasets that approach the MI300's 128 GB limit. The newer CDNA 4.0 architecture and 3 nm process indicate a more modern design, and the 1:1 FP16 ratio suggests consistent precision handling without packed-mode trade-offs. The single 16-pin connector simplifies cabling compared to the MI300's two 8-pin connectors.

Neither card has recorded performance data, so the verdict reflects theoretical ceilings, not measured results. The MI300 wins on raw compute throughput and transistor count. The MI350P wins on memory capacity, bandwidth, and process technology. Users prioritizing peak FLOPs should choose the MI300; users prioritizing memory footprint and bandwidth should choose the MI350P. Both carry the same 600 W TDP and require a 1000 W PSU, so power constraints do not differentiate them. With no benchmark evidence available, the choice hinges entirely on workload memory requirements versus arithmetic intensity.