AMD Instinct MI350X vs NVIDIA B300 Comparison

Head-to-Head Benchmarks

The recorded data shows no direct head-to-head benchmark results between the AMD Instinct MI350X and the NVIDIA B300. Both accelerators hold a 50th percentile position among all GPUs in the database, with an average benchmark score of zero. The wins column registers zero for each part, meaning no competitive measurement has been logged for either accelerator against the other. This absence of comparative scores does not diminish the value of the specifications on file; instead, it highlights that any performance assessment must rely on architectural and specification analysis rather than empirical test results. The database currently contains no frame rate data, no compute throughput comparisons, and no latency measurements for either product. Consequently, the analysis below interprets the available technical characteristics to project relative strengths in various workloads, while acknowledging that real-world validation remains pending.

Where Each One Wins

The AMD Instinct MI350X uses a 16384 shading unit array paired with 1024 texture mapping units, producing a texture rate of 2,252.8 GTexel/s. This configuration gives it a substantial lead in raw texture throughput, which directly benefits workloads that stress texture fetch and filtering operations. The MI350X also carries 288 GB of HBM3e memory on an 8192 bit bus, delivering 8.19 TB/s of bandwidth. That memory capacity is double what the NVIDIA B300 offers, and the bandwidth advantage is roughly 2x as well. For large language model inference, scientific simulation datasets, or any workload that must keep massive working sets resident on the accelerator, the MI350X holds a clear edge. The 3 nm process node from TSMC allows AMD to pack 185,000 million transistors into a 2380 mm² die, achieving a transistor density of 77.7M per mm². This density supports the large cache hierarchy and compute resources without ballooning power consumption beyond the 1000 W TDP.

The NVIDIA B300 counters with a different set of strengths. Its 18944 shading units exceed the MI350X count by 2560 units, and the boost clock of 2032 MHz is slightly below the MI350X 2200 MHz boost, yet the B300 still reaches 76.99 TFLOPS of FP32 throughput versus 72.09 TFLOPS for the MI350X. The B300 also integrates 592 tensor cores, a feature the MI350X does not list at all. The FP16 performance gap is enormous: the B300 delivers 1,231.8 TFLOPS with a 16:1 ratio, while the MI350X delivers only 72.09 TFLOPS at a 1:1 ratio. That makes the B300 the clear choice for AI training and inference workloads that rely heavily on reduced-precision matrix math. The B300 also includes 24 ROPs, giving it a pixel rate of 48.77 GPixel/s, whereas the MI350X lists zero ROPs and zero pixel throughput. For any rendering or rasterization task, the B300 is the only viable option between the two.

The B300 uses a 5 nm process from TSMC and packs 104,000 million transistors, though its die size is not recorded. The 1400 W TDP is 400 W higher than the MI350X, and the suggested PSU of 1800 W reflects that increased power appetite. The B300 ships in an SXM Module form factor, while the MI350X uses an OAM Module, indicating different physical integration paths in server chassis.

Architecture Differences

The architectural split between these two accelerators is fundamental. AMD uses CDNA 4.0 with the MI350 256CU chip, targeting compute throughput with a balanced FP32 and FP16 ratio of 1:1. The 72.09 TFLOPS figure applies equally to both precisions, suggesting that AMD optimized this architecture for workloads where full precision is non-negotiable, such as certain scientific computing tasks that cannot tolerate reduced mantissa widths. The lack of tensor cores in the recorded data reinforces this interpretation: the MI350X is a general-purpose compute engine rather than a matrix-math specialist.

NVIDIA builds the B300 on the Blackwell Ultra architecture with the GB110 chip. The 16:1 FP16 ratio indicates aggressive use of reduced precision, a design choice that prioritizes AI workloads where the 1,231.8 TFLOPS FP16 throughput can accelerate training loops and inference batches. The 592 tensor cores are the hardware manifestation of this focus, providing dedicated matrix multiplication units that offload work from the shading units. The B300 also lists a memory clock of 2000 MHz with 8 Gbps effective speed, identical to the MI350X memory clock, but the B300's 4096 bit bus width halves the bandwidth to 4.10 TB/s.

The process node difference matters for thermal density. The MI350X uses a 3 nm process, allowing more transistors per square millimeter (77.7M vs an unrecorded figure for the B300). The MI350X die measures 2380 mm², which is among the largest in the database, while the B300 die size is not recorded. The MI350X transistor count of 185,000 million exceeds the B300's 104,000 million by 81,000 million, yet the B300 draws 400 W more power. This suggests NVIDIA chose higher clock speeds and wider FP16 execution paths at the cost of efficiency, while AMD prioritized density and bandwidth.

Specification Differences

The two accelerators diverge on nearly every measurable specification. The MI350X has a base clock of 1000 MHz and a boost clock of 2200 MHz, while the B300 has a higher base clock of 1665 MHz but a lower boost of 2032 MHz. The MI350X memory capacity is 288 GB, exactly double the B300's 144 GB. The memory bus width is 8192 bit for AMD versus 4096 bit for NVIDIA, yielding 8.19 TB/s versus 4.10 TB/s bandwidth. Shading units: 16384 for the MI350X, 18944 for the B300. Texture mapping units: 1024 for AMD, 592 for NVIDIA. Raster operation units: 0 for AMD, 24 for NVIDIA. Tensor cores: none listed for AMD, 592 for NVIDIA. Pixel rate: 0 MPixel/s for AMD, 48.77 GPixel/s for NVIDIA. Texture rate: 2,252.8 GTexel/s for AMD, 1,202.9 GTexel/s for NVIDIA. FP32: 72.09 TFLOPS for AMD, 76.99 TFLOPS for NVIDIA. FP16: 72.09 TFLOPS for AMD, 1,231.8 TFLOPS for NVIDIA. TDP: 1000 W for AMD, 1400 W for NVIDIA. Suggested PSU: 1400 W for AMD, 1800 W for NVIDIA. Form factor: OAM Module for AMD, SXM Module for NVIDIA. Power connectors: none for AMD, not listed for NVIDIA. The MI350X dimensions are 102 mm length and 165 mm width, while the B300 dimensions are not recorded. Release dates: the MI350X launched on June 11, 2025, and the B300 on September 10, 2025. The B300 has a production status of Active, while the MI350X status is not listed. The B300 predecessor is Server Hopper and its successor is Server Rubin; the MI350X predecessor is Radeon Instinct with no successor listed.

FAQ

Q: Which accelerator has more memory bandwidth?

A: The AMD Instinct MI350X delivers 8.19 TB/s across an 8192 bit bus, which is exactly double the NVIDIA B300's 4.10 TB/s over a 4096 bit bus.

Q: How do the two compare in FP16 compute throughput?

A: The NVIDIA B300 reaches 1,231.8 TFLOPS FP16 with a 16:1 ratio, while the AMD MI350X delivers 72.09 TFLOPS FP16 at a 1:1 ratio, making the B300 approximately 17 times faster in reduced-precision workloads.

Q: What is the transistor count difference?

A: The MI350X packs 185,000 million transistors on a 2380 mm² die using a 3 nm process, while the B300 has 104,000 million transistors on an unrecorded die size using a 5 nm process.

Q: Do either of these cards support display outputs?

A: No. Both the AMD Instinct MI350X and the NVIDIA B300 list "No outputs" for display connections, consistent with their server accelerator roles.

Q: Which part has a higher boost clock?

A: The AMD MI350X boosts to 2200 MHz, which is 168 MHz higher than the NVIDIA B300's boost clock of 2032 MHz. However, the B300 has a higher base clock at 1665 MHz versus 1000 MHz for the MI350X.

Q: What are the power requirements for each module?

A: The MI350X has a TDP of 1000 W and a suggested PSU of 1400 W, while the B300 has a TDP of 1400 W and a suggested PSU of 1800 W. The MI350X lists no power connectors, while the B300 does not record power connector details.