AMD Instinct MI300X vs NVIDIA B200 Comparison

The NVIDIA B200 and AMD Instinct MI300X are both top-tier server accelerators, each built on a 5 nm TSMC process. In the sole head-to-head benchmark available, the Geekbench OpenCL test, the NVIDIA B200 achieves a score of 345,482, while the AMD Instinct MI300X scores 317,994. This puts the B200 8.6% ahead, a significant margin across high-performance computing where every percentage point translates to substantial real-world throughput differences.

Where Each One Wins

The data presents a clear picture: the NVIDIA B200 is the outright winner in the only direct benchmark comparison available. It claims victory in the Geekbench OpenCL test, which is a strong indicator of general compute performance across a wide range of workloads. The B200's score of 345,482 is not just a marginal win; it represents a full 8.6% performance advantage over the MI300X's 317,994 score. For practical purposes, this means that in compute-heavy tasks like AI inference, scientific simulations, or data processing, the B200 is poised to finish jobs faster.

However, the AMD Instinct MI300X is not without its own distinct advantages that make it a winner in specific scenarios. The most glaring difference is memory capacity. The MI300X comes equipped with 192 GB of HBM3 memory, which is more than double the B200's 90 GB of HBM3e. This massive capacity is a critical advantage for workloads that require holding enormous datasets, such as large language model (LLM) inference with very large batch sizes or training models that do not fit within a smaller memory footprint. The MI300X also has a higher memory bandwidth at 5.32 TB/s compared to the B200's 4.10 TB/s, further cementing its role as a memory-centric accelerator. While the B200 wins on raw compute speed, the MI300X wins on the ability to handle larger problems in a single accelerator.

FAQ

Q: Is the NVIDIA B200 faster than the AMD Instinct MI300X in the benchmark data?

A: Yes. In the Geekbench OpenCL test, the B200 scores 345,482, which is 8.6% higher than the MI300X's score of 317,994.

Q: Which GPU has more memory, and why does that matter?

A: The AMD Instinct MI300X has 192 GB of HBM3, while the NVIDIA B200 has 90 GB of HBM3e. The larger capacity on the MI300X allows it to hold significantly larger models or datasets in memory, which can be a crucial advantage for complex AI workloads.

Q: Does the MI300X have any performance advantage over the B200?

A: The MI300X offers a higher theoretical memory bandwidth of 5.32 TB/s compared to the B200's 4.10 TB/s, which can benefit memory-bound tasks. However, in the actual benchmark, the B200 still held a performance lead.

Q: How does the B200 compare to its nearest rival, the NVIDIA H200 NVL?

A: The B200's average benchmark score is 345,482, which is 3.2% higher than the H200 NVL's score of 334,891.

Q: What is the power draw difference between the two accelerators?

A: The NVIDIA B200 has a TDP of 1000 W, while the AMD Instinct MI300X has a lower TDP of 750 W. The B200 also requires a higher suggested PSU of 1400 W compared to the MI300X's 1150 W.

Q: Are either of these cards suitable for a desktop gaming PC?

A: No. Both are server modules (SXM for the B200 and OAM for the MI300X), have no display outputs, and are designed for datacenter deployment.

Head-to-Head Benchmarks

The head-to-head comparison is straightforward, with only one data point: Geekbench OpenCL. In this test, the NVIDIA B200 scores 345,482, outperforming the AMD Instinct MI300X's score of 317,994. The delta of 8.6% is a substantial margin. To put it in perspective, this is more than double the B200's 3.2% lead over the NVIDIA H200 NVL, its closest rival. This suggests that while the B200 is a clear step above its immediate predecessor, its advantage over the AMD offering is even more pronounced.

Looking at the broader competitive landscape reinforces this result. The MI300X's average score of 317,994 places it 5% behind the NVIDIA H200 NVL. The B200, on the other hand, is 3.2% ahead of the H200. This creates a clear hierarchy where the B200 sits at the top, followed by the H200 NVL, and then the MI300X. Furthermore, the B200 holds a 16.8% lead over the NVIDIA L40S, while the MI300X is only 7.5% ahead of that same L40S. This data consistently shows the B200 as the superior performer in raw compute, with a lead that grows when compared to lower-tier accelerators.

Specification Differences

The specification sheets for these two accelerators reveal distinct design philosophies. The NVIDIA B200 is built on the Blackwell architecture (chip GB100), while the AMD Instinct MI300X uses CDNA 3.0 (chip Aqua Vanjaram). Both are manufactured on a 5 nm process at TSMC, but AMD's chip is significantly larger in terms of transistor count, packing 153,000 million transistors on a 1017 mm² die. NVIDIA's B200 has 104,000 million transistors, but its die size is not listed.

Clock speeds are another point of difference. The MI300X has a higher base clock of 1000 MHz and a boost clock of 2100 MHz, compared to the B200's 700 MHz base and 1965 MHz boost. Despite lower clocks, the B200 achieves a higher benchmark score, indicating architectural efficiency. Memory configurations also diverge sharply. The B200 uses 90 GB of HBM3e over a 4096-bit bus, while the MI300X uses 192 GB of HBM3 over a wider 8192-bit bus. This wider bus gives the MI300X a higher theoretical bandwidth of 5.32 TB/s versus 4.10 TB/s for the B200. The B200's memory runs at 8 Gbps effective, while the MI300X's runs at 5.2 Gbps effective.

In terms of compute units, the B200 has 18,944 shading units, 592 TMUs, and 24 ROPs. The MI300X has slightly more shading units at 19,456, but a much higher number of TMUs at 1,216, and its ROP count is listed as 0. The B200's FP32 throughput is 74.45 TFLOPS, while the MI300X reaches 81.72 TFLOPS. However, a major divergence appears in FP16 performance: the B200 lists a massive 1,191.2 TFLOPS (16:1), while the MI300X lists 81.72 TFLOPS (1:1), indicating a specialized tensor-core-like path for the B200. Power requirements also differ, with the B200 rated at 1000 W TDP and the MI300X at 750 W TDP. The B200 is an SXM Module, while the MI300X is an OAM Module, and both use a PCIe 5.0 x16 interface.

Architecture Differences

The architectural split between these two accelerators is fundamental. The NVIDIA B200 is a Blackwell-generation part (successor to Server Hopper), built around the GB100 chip. Its design appears heavily optimized for AI and machine learning workloads, as evidenced by its 592 tensor cores and the staggering FP16 performance figure of 1,191.2 TFLOPS, which is a 16:1 ratio compared to its FP32 output. This suggests a dedicated and powerful matrix-math engine that is separate from the standard shader cores. The B200's relatively low 24 ROPs underscore that this is not a graphics-oriented chip.

In contrast, the AMD Instinct MI300X is based on the CDNA 3.0 architecture (generation Instinct MIx, predecessor Radeon Instinct). It does not have dedicated "tensor cores" in the NVIDIA sense, and its FP16 performance is identical to its FP32 performance at 81.72 TFLOPS (1:1). This indicates a more general-purpose compute design. The MI300X compensates with a massive 192 GB memory pool and a wider 8192-bit memory bus. The substantial transistor count (153,000 million) and larger die size (1017 mm²) suggest a design focused on maximizing memory capacity and bandwidth to feed its compute units. The B200's transistor density is not listed, but its higher benchmark score with fewer transistors points to a more efficient architecture per transistor for the tested workload.

The Verdict

Based strictly on the benchmark data, the NVIDIA B200 is the superior choice for raw computational performance. Its Geekbench OpenCL score of 345,482 is 8.6% higher than the AMD Instinct MI300X's score of 317,994. It also holds a 3.2% lead over its nearest rival, the NVIDIA H200 NVL, and an 8.6% lead over the MI300X. If the priority is maximum processing speed for a given task, the B200 is the clear pick from this data.

However, the AMD Instinct MI300X makes a strong case for a different use case. Its 192 GB of HBM3 memory is more than double the B200's 90 GB, and its memory bandwidth is higher at 5.32 TB/s. For workloads where the entire model or dataset must reside in GPU memory, the MI300X is the only one of the two that can handle these larger problems in a single module. Its lower TDP of 750 W compared to the B200's 1000 W is also a factor for power-constrained datacenters. The choice is clear: the B200 wins on speed, while the MI300X wins on capacity and power efficiency.