AMD Instinct MI308X vs NVIDIA B200 SXM6 Comparison

Head-to-Head Benchmarks

The recorded data contains no benchmark scores for either the AMD Instinct MI308X or the NVIDIA B200 SXM6. The average benchmark score for both accelerators is 0, and the percentile versus all GPUs is 50 for each, indicating that no direct performance measurements are available in the database. Consequently, there are no wins recorded for either part, with the win count sitting at 0 for both the MI308X and the B200 SXM6.

Without measured scores, the head-to-head comparison must rely on the architectural specifications and memory subsystems documented in the database. The MI308X delivers 81.72 TFLOPS of FP32 throughput and 81.72 TFLOPS of FP16 (1:1), while the B200 SXM6 provides 69.34 TFLOPS for both FP32 and FP16 (1:1). This gives the AMD part a 12.38 TFLOPS advantage in raw floating-point compute per module, which translates to approximately 17.9% higher throughput in peak FP32 and FP16 operations.

The memory bandwidth comparison shows a clear NVIDIA advantage. The B200 SXM6 reaches 8.19 TB/s with HBM3e across an 8192-bit bus, while the MI308X attains 5.32 TB/s with HBM3 over the same 8192-bit bus width. The NVIDIA part leads by 2.87 TB/s, a 53.9% bandwidth advantage. This differential is significant for memory-bound workloads, as the B200 can move data substantially faster per unit time.

Texture throughput also favors the AMD accelerator. The MI308X records 2,553.6 GTexel/s from 1,216 TMUs, whereas the B200 SXM6 manages 1,083.4 GTexel/s from 592 TMUs. The AMD part is 1,470.2 GTexel/s ahead, more than doubling the NVIDIA texture rate. Pixel throughput tells a different story: the B200 SXM6 produces 43.92 GPixel/s from 24 ROPs, while the MI308X reports 0 MPixel/s with no ROPs enumerated, indicating the AMD module is not designed for rasterization output.

Architecture Differences

The two accelerators come from different architecture generations and chip designs. The MI308X uses the Aqua Vanjaram chip built on CDNA 3.0, part of the Instinct (MIx) generation, fabricated on a 5 nm process at TSMC. The B200 SXM6 uses the GB100 chip on the Blackwell architecture, part of the Server Blackwell (Bxx) generation, also fabricated on 5 nm at TSMC. Both share the same process node and foundry, but the transistor counts differ markedly: the MI308X integrates 153,000 million transistors on a 1017 mm² die, while the B200 SXM6 packs 208,000 million transistors on a 1628 mm² die. The AMD chip has a higher transistor density at 150.4M per mm² versus 127.8M per mm² for NVIDIA.

Memory configurations diverge in both capacity and type. The MI308X carries 192 GB of HBM3, whereas the B200 SXM6 carries 180 GB of HBM3e. The NVIDIA module uses a faster memory generation, which explains its higher bandwidth despite slightly lower capacity. Both parts use an 8192-bit memory bus, but the B200 SXM6 runs its memory at 2000 MHz with 8 Gbps effective speed, while the MI308X runs at 1300 MHz with 5.2 Gbps effective speed. The clock speeds also differ substantially in the compute domain: the MI308X has a 1000 MHz base clock and 2100 MHz boost clock, while the B200 SXM6 has a 120 MHz base clock and 1830 MHz boost clock.

The shading unit counts are similar, with the MI308X at 19,456 shading units and the B200 SXM6 at 18,944, a difference of 512 units. Texture mapping units differ more significantly: the MI308X has 1,216 TMUs versus 592 on the B200. The B200 SXM6 includes 592 tensor cores and 24 ROPs, while the MI308X lists no tensor cores and no ROPs in the database. The NVIDIA part also has a tensor core count that matches its TMU count, suggesting a different compute organization.

Power and interface specifications set the two apart as well. The MI308X has a TDP of 750 W with a suggested PSU of 1150 W, while the B200 SXM6 has a TDP of 1000 W with a suggested PSU of 1400 W. The bus interface differs: the MI308X uses PCIe 5.0 x16, while the B200 SXM6 uses PCIe 6.0 x16. Neither accelerator has display outputs, and both list DirectX, OpenGL, and Vulkan APIs as N/A, confirming their compute-only orientation. The MI308X is an OAM module, while the B200 SXM6 is an SXM module.

Release timing shows the MI308X launched on 2023-12-05, while the B200 SXM6 launched on 2024-10-31, nearly 11 months later. The B200 SXM6 has an active production status and lists a successor named Server Rubin, while the MI308X does not have a documented production status or successor. The NVIDIA part also has a recorded launch MSRP of 34,999 USD, which can be stated once as a reference point.

Where Each One Wins

Based on the recorded specifications, the MI308X wins in raw compute throughput. Its FP32 and FP16 figures of 81.72 TFLOPS exceed the B200 SXM6's 69.34 TFLOPS for both formats, making it the stronger choice for workloads that saturate arithmetic units, such as dense matrix operations or high-precision simulation kernels. The 12.38 TFLOPS gap is a direct performance margin that does not depend on software optimization or memory access patterns.

The MI308X also holds a decisive advantage in texture throughput. With 2,553.6 GTexel/s versus 1,083.4 GTexel/s, the AMD module delivers more than double the texel processing rate. For applications that perform heavy texture sampling, such as certain scientific visualization or image processing pipelines, the MI308X offers a clear lead. The higher TMU count of 1,216 versus 592 substantiates this advantage, as each texture unit contributes to the aggregate rate.

The B200 SXM6 wins in memory bandwidth, and by a wide margin. The 8.19 TB/s HBM3e interface outperforms the MI308X's 5.32 TB/s HBM3, giving the NVIDIA part a 53.9% bandwidth advantage. For large language model inference or training where parameter weights and activations must stream through the memory subsystem, higher bandwidth directly translates to faster data movement and reduced idle compute time. The B200 SXM6 also uses faster memory clocks at 2000 MHz versus 1300 MHz, which contributes to this bandwidth lead.

The B200 SXM6 additionally records pixel throughput of 43.92 GPixel/s from its 24 ROPs, while the MI308X reports 0 MPixel/s with no ROPs. In the narrow case where rasterization output is required, the NVIDIA part is the only one with any documented capability. The B200 SXM6 also includes 592 tensor cores, which are absent from the MI308X specification, indicating a structural difference in how each module handles matrix multiplication workloads.

The NVIDIA part carries more transistors (208,000 million versus 153,000 million) and a larger die (1628 mm² versus 1017 mm²), which may translate to higher aggregate capability in memory-heavy or tensor-focused tasks. However, the MI308X achieves higher transistor density (150.4M per mm² versus 127.8M per mm²), suggesting more efficient use of silicon area for the compute units it does employ.

FAQ

Q: Which accelerator has higher FP32 compute throughput?

A: The AMD Instinct MI308X delivers 81.72 TFLOPS of FP32, which is 12.38 TFLOPS higher than the NVIDIA B200 SXM6's 69.34 TFLOPS.

Q: What is the memory bandwidth difference between the two?

A: The NVIDIA B200 SXM6 provides 8.19 TB/s of bandwidth with HBM3e, while the AMD Instinct MI308X provides 5.32 TB/s with HBM3, a difference of 2.87 TB/s in favor of NVIDIA.

Q: How do the transistor counts compare?

A: The NVIDIA B200 SXM6 has 208,000 million transistors on a 1628 mm² die, while the AMD Instinct MI308X has 153,000 million transistors on a 1017 mm² die. The AMD chip has a higher density at 150.4M transistors per mm² versus 127.8M for NVIDIA.

Q: Does either accelerator support display outputs or standard graphics APIs?

A: Neither part has display outputs. Both list DirectX, OpenGL, and Vulkan as N/A. The AMD Instinct MI308X reports 0 MPixel/s pixel rate, while the NVIDIA B200 SXM6 reports 43.92 GPixel/s.

Q: What are the power requirements for each module?

A: The AMD Instinct MI308X has a TDP of 750 W and a suggested PSU of 1150 W. The NVIDIA B200 SXM6 has a TDP of 1000 W and a suggested PSU of 1400 W.

Q: What is the release timeline for these accelerators?

A: The AMD Instinct MI308X was released on 2023-12-05, while the NVIDIA B200 SXM6 was released on 2024-10-31. The NVIDIA part is listed as active in production, and its predecessor is Server Hopper.

The Verdict

The data in the database does not include any benchmark scores for either the AMD Instinct MI308X or the NVIDIA B200 SXM6, so a definitive performance verdict cannot be derived from measured results. Instead, the specification sheets define two different compute profiles. The MI308X leads in arithmetic throughput with 81.72 TFLOPS FP32 and FP16, and it doubles the texture rate at 2,553.6 GTexel/s. The B200 SXM6 leads in memory bandwidth at 8.19 TB/s, offers 43.92 GPixel/s of pixel throughput, and integrates 592 tensor cores that the AMD part does not list.

For workloads that are compute-bound and require high FP32 or FP16 rates, the MI308X shows a numeric advantage that is directly documented in its peak throughput figures. For workloads that are memory-bound or rely on tensor operations, the B200 SXM6 presents a stronger specification with 53.9% more bandwidth and dedicated tensor hardware. The NVIDIA module also operates at a higher TDP of 1000 W versus 750 W, which may reflect the additional power drawn by its larger transistor count and faster memory.

The MI308X uses an older HBM3 memory type with a lower clock speed, while the B200 SXM6 uses HBM3e at a higher effective data rate. The bus interface also differs, with the B200 SXM6 supporting PCIe 6.0 x16 versus PCIe 5.0 x16 on the MI308X, a generational step that may affect host-to-device transfer rates in systems that support the newer standard. The B200 SXM6 has a documented successor, Server Rubin, and an active production status, whereas the MI308X has no successor listed.

The choice between these two accelerators hinges on the specific demands of the target workload. The MI308X provides higher raw compute and texture throughput, making it suited for arithmetic-heavy tasks. The B200 SXM6 provides higher memory bandwidth, tensor cores, and the only documented pixel output, making it suited for memory-intensive and tensor-based applications. The database currently offers no measured benchmark wins for either part, so final selection should be guided by the architectural alignment with the workload profile rather than empirical performance data.