AMD Instinct MI300 vs NVIDIA B300 Comparison

Head-to-Head Benchmarks

The recorded database contains no benchmark scores for either the AMD Instinct MI300 or the NVIDIA B300. Both entries show an average benchmark score of zero, and the head-to-head benchmark array is empty. Consequently, there are no measurable wins to attribute to either accelerator in compute workloads, graphics throughput, or memory-bound tasks. The percentile versus all GPUs is identical for both parts at 50, indicating that without submitted workloads, neither device can be ranked above the other in any performance category.

The absence of recorded data does not reflect poorly on either product; it simply means the database has not yet collected validated results. For users seeking comparative performance numbers, the available specifications provide the only basis for analysis. The MI300 delivers 47.87 TFLOPS of FP32 throughput, while the B300 delivers 76.99 TFLOPS. That difference, 29.12 TFLOPS, places the NVIDIA part roughly 60% ahead in raw single-precision compute based on manufacturer-stated figures. In FP16, the gap is far larger: the MI300 lists 47.87 TFLOPS with a 1:1 ratio, while the B300 lists 1,231.8 TFLOPS with a 16:1 ratio. The B300 therefore advertises more than 25 times the FP16 throughput, though the different ratio conventions make a direct comparison less straightforward.

Texture rate favors the AMD part. The MI300 shows 1,496.0 GTexel/s against 1,202.9 GTexel/s for the B300, a lead of roughly 24%. Pixel rate, however, reverses the order: the MI300 reports 0 MPixel/s, while the B300 reports 48.77 GPixel/s. The MI300 has no ROP units, which explains the zero pixel throughput, while the B300 includes 24 ROPs. Memory bandwidth also splits the two: the MI300 reaches 5.32 TB/s over an 8192-bit bus, while the B300 reaches 4.10 TB/s over a 4096-bit bus. The AMD design leads by approximately 30% in bandwidth.

Architecture Differences

The two accelerators diverge sharply in silicon design and feature allocation. The AMD Instinct MI300 uses the Aqua Vanjaram chip, built on the CDNA 3.0 architecture, produced on a 5 nm process at TSMC. The NVIDIA B300 uses the GB110 chip, built on the Blackwell Ultra architecture, also produced on a 5 nm process at TSMC. Both share the same foundry and node, but their transistor budgets differ considerably. The MI300 integrates 153,000 million transistors on a 1017 mm² die, yielding a transistor density of 150.4 million per square millimeter. The B300 integrates 104,000 million transistors, with no die size or density figure recorded.

Shader configuration favors the NVIDIA part. The B300 carries 18,944 shading units, while the MI300 has 14,080. Texture mapping units favor AMD at 880 versus 592. Raster operation units favor NVIDIA decisively: the MI300 has 0 ROPs, while the B300 has 24. Neither product includes dedicated ray tracing cores in the database. The B300 does list 592 tensor cores, while the MI300 lists no tensor core count at all. The MI300 also lacks any pixel rate, consistent with its zero-ROP configuration. Clock behavior differs as well. The MI300 runs at a 1000 MHz base and 1700 MHz boost, while the B300 runs at 1665 MHz base and 2032 MHz boost. The NVIDIA part operates at substantially higher frequencies, which contributes to its higher FP32 figure despite a narrower memory bus.

Memory architecture shows two distinct approaches. The MI300 uses 128 GB of HBM3 on an 8192-bit bus, with memory clocked at 1300 MHz, 5.2 Gbps effective. The B300 uses 144 GB of HBM3e on a 4096-bit bus, with memory clocked at 2000 MHz, 8 Gbps effective. The MI300 has a wider bus and higher total bandwidth, while the B300 uses a faster memory type in smaller quantity. Capacity favors NVIDIA by 16 GB. The B300 also lists a 16:1 FP16 ratio, suggesting a heavily optimized tensor path, while the MI300 lists a 1:1 FP16 ratio, indicating a more general-purpose compute orientation.

Power and packaging differ dramatically. The MI300 has a TDP of 600 W with two 8-pin power connectors and a suggested PSU of 1000 W. The B300 has a TDP of 1400 W, no recorded power connector detail, and a suggested PSU of 1800 W. The B300 is an SXM module, while the MI300 is a 267 mm by 111 mm card. Both use PCIe 5.0 x16, and neither has display outputs. The MI300 was released on January 3, 2023, while the B300 was released on September 10, 2025. The MI300 lists Radeon Instinct as its predecessor, while the B300 lists Server Hopper as its predecessor and Server Rubin as its successor.

The Verdict

The recorded data supports a clear division of strengths. For FP32 compute, the B300 leads with 76.99 TFLOPS versus 47.87 TFLOPS, a 60% advantage. For FP16 throughput, the B300 leads by a factor of roughly 25, assuming the stated ratios are used as advertised. For memory bandwidth, the MI300 leads with 5.32 TB/s versus 4.10 TB/s, a 30% advantage. For texture throughput, the MI300 leads with 1,496.0 GTexel/s versus 1,202.9 GTexel/s, a 24% advantage. For pixel throughput, the B300 leads with 48.77 GPixel/s, while the MI300 provides none.

The B300 is the stronger candidate for workloads that depend on FP32 or FP16 arithmetic, tensor operations, or rasterization. The MI300 is the stronger candidate for memory-bound tasks and texture-heavy workloads, where its wider bus and higher bandwidth provide measurable headroom. The MI300 also operates at a far lower TDP of 600 W against 1400 W, which the database records as a substantial power difference. Neither part has benchmark scores, so these conclusions rest entirely on specification-level analysis.

Users selecting based on raw compute density should favor the B300. Users selecting based on memory throughput or power envelope should favor the MI300. The choice is not ambiguous in the data: each part wins in distinct categories, and no overall winner emerges without application-specific workload results.

FAQ

Q: Which accelerator has higher FP32 performance?

A: The NVIDIA B300 lists 76.99 TFLOPS of FP32, while the AMD Instinct MI300 lists 47.87 TFLOPS. The B300 leads by 29.12 TFLOPS.

Q: Which accelerator has more memory bandwidth?

A: The AMD Instinct MI300 has 5.32 TB/s of bandwidth over an 8192-bit bus. The NVIDIA B300 has 4.10 TB/s over a 4096-bit bus. The MI300 leads by roughly 30%.

Q: How much memory does each accelerator have?

A: The AMD Instinct MI300 has 128 GB of HBM3. The NVIDIA B300 has 144 GB of HBM3e. The B300 has 16 GB more capacity.

Q: What are the power requirements?

A: The AMD Instinct MI300 has a TDP of 600 W and a suggested PSU of 1000 W. The NVIDIA B300 has a TDP of 1400 W and a suggested PSU of 1800 W.

Q: Does either accelerator have display outputs?

A: No. Both the AMD Instinct MI300 and the NVIDIA B300 list no display outputs.

Q: What process node do both chips use?

A: Both the AMD Instinct MI300 and the NVIDIA B300 are manufactured on a 5 nm process at TSMC.

Where Each One Wins

The AMD Instinct MI300 wins in memory bandwidth, texture rate, and power efficiency. Its 5.32 TB/s bandwidth exceeds the B300 by 1.22 TB/s. Its 1,496.0 GTexel/s texture rate exceeds the B300 by 293.1 GTexel/s. Its 600 W TDP is less than half of the B300's 1400 W, and its suggested PSU of 1000 W is 800 W lower. These attributes favor workloads that stream large datasets, apply heavy texture sampling, or operate under strict power constraints. The MI300's 153,000 million transistors on a 1017 mm² die also represent the larger silicon investment, with a transistor density of 150.4 million per square millimeter.

The NVIDIA B300 wins in FP32 compute, FP16 compute, pixel rate, memory capacity, and clock speed. Its 76.99 TFLOPS FP32 figure exceeds the MI300 by 29.12 TFLOPS. Its 1,231.8 TFLOPS FP16 figure represents a 16:1 ratio, indicating a heavily tensor-optimized path that the MI300 does not match with its 1:1 ratio. Its 48.77 GPixel/s pixel rate stands against a zero value for the MI300. Its 144 GB memory capacity exceeds the MI300 by 16 GB. Its 1665 MHz base and 2032 MHz boost clocks both exceed the MI300's 1000 MHz base and 1700 MHz boost. The B300 also lists 18,944 shading units against 14,080, and 592 tensor cores against no listed count.

For a deployment targeting dense arithmetic, tensor-heavy inference, or graphics-style rasterization, the B300 delivers the recorded advantages. For a deployment targeting memory throughput, texture processing, or reduced power draw, the MI300 delivers the recorded advantages. The database contains no overlapping benchmark results, so these category wins represent the only defensible separation between the two parts.