GPU Comparison

AMD Instinct MI300X and NVIDIA A10G occupy very different strata of the accelerator market, and the benchmark data reflects a decisive performance gap. The MI300X delivers a Geekbench OpenCL score of 317,994, placing it in the 100th percentile of all GPUs, while the A10G scores 158,063, landing in the 97th percentile. This represents a 101.2% advantage for the AMD part in the single head-to-head benchmark available, a margin that dwarfs the differences typically seen between adjacent generations of server hardware.

Head-to-Head Benchmarks

The only direct comparison available is Geekbench OpenCL, and the result is unambiguous. The AMD Instinct MI300X scores 317,994 against the NVIDIA A10G's 158,063, yielding a delta of 101.2% in favor of AMD. This is not a marginal victory; it is a doubling of raw compute throughput in a synthetic workload that stresses general-purpose shader performance. For context, the MI300X's score places it 7.5% ahead of the NVIDIA L40S and 10.7% ahead of the NVIDIA RTX 6000 Ada Generation, while sitting 5% behind the NVIDIA H200 NVL and 8% behind the NVIDIA B200. The A10G, by contrast, is only 1.1% ahead of the Tesla V100 PCIe 32 GB and 9.3% ahead of the AMD Instinct MI100, but trails the Radeon Pro W6800X by 5.4% and the A100 PCIe 40 GB by 6.5%.

The sheer magnitude of the OpenCL delta suggests architectural scaling rather than mere clock speed advantages. The MI300X achieves 81.72 TFLOPS FP32, while the A10G manages 31.52 TFLOPS FP32. That 2.59x raw throughput difference is compressed to a 2.01x benchmark difference, meaning the A10G extracts relatively more efficiency from its silicon per FLOP in this particular test. Still, the absolute numbers leave no room for ambiguity: the MI300X is the dominant part in raw compute, and the A10G's 158,063 score is closer to the MI300X's nearest rival at the low end (the RTX 6000 Ada at 287,237) than to the MI300X itself.

Where Each One Wins

The MI300X wins the only head-to-head benchmark, so the use-case split must be inferred from the score distribution and architectural characteristics. In pure compute throughput, the MI300X is categorically superior. Its 192 GB of HBM3 memory with 5.32 TB/s bandwidth dwarfs the A10G's 24 GB of GDDR6 at 600.2 GB/s, making the AMD part the clear choice for workloads that exceed 24 GB of working set or that saturate memory bandwidth, such as large language model inference, scientific simulation, or high-resolution rendering. The 8192-bit memory bus versus the A10G's 384-bit bus underscores this disparity.

The A10G, however, has strengths in specific operational contexts. Its 150 W TDP is one-fifth of the MI300X's 750 W, and it fits in a single-slot form factor with an 8-pin EPS power connector. The MI300X is an OAM Module with no power connectors and requires a 1150 W suggested PSU. For dense server deployments where power density and physical footprint are constrained, the A10G offers usable compute without the infrastructure demands of the MI300X. Additionally, the A10G supports DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4, whereas the MI300X reports N/A for all graphics APIs. This makes the A10G the only one of the two capable of any graphics-oriented workload, even if its primary purpose is compute.

The A10G also has a longer operational history, with a 2021 release date and end-of-life production status, while the MI300X was released in December 2023 and has no production status listed. The A10G's predecessor is Tesla Turing, and its successor is Server Ada, indicating a mature product line. The MI300X's predecessor is Radeon Instinct, suggesting a pivot to a new naming scheme.

Architecture Differences

The architectural gap between these two parts is generational. The MI300X uses the CDNA 3.0 architecture on a 5 nm TSMC process, packing 153,000 million transistors into a 1017 mm² die. The A10G uses the Ampere architecture on an 8 nm Samsung process, with 28,300 million transistors on a 628 mm² die. This yields transistor densities of 150.4M per mm² for the AMD part versus 45.1M per mm² for the NVIDIA part, a 3.3x density advantage that explains the MI300X's massive compute lead despite similar die sizes.

The compute units reflect this difference. The MI300X has 19,456 shading units and 1,216 TMUs, while the A10G has 9,216 shading units and 288 TMUs. The MI300X's texture rate is 2,553.6 GTexel/s versus the A10G's 492.5 GTexel/s. However, the MI300X has 0 ROPs and a pixel rate of 0 MPixel/s, while the A10G has 96 ROPs and a 164.2 GPixel/s pixel rate. This confirms the MI300X is a pure compute accelerator with no rasterization pipeline, while the A10G retains some graphics capability as evidenced by its API support.

Memory architecture differs fundamentally. The MI300X uses 192 GB of HBM3 with a 5.32 TB/s bandwidth, while the A10G uses 24 GB of GDDR6 with 600.2 GB/s bandwidth. The MI300X's memory clock is 1300 MHz (5.2 Gbps effective), while the A10G's is 1563 MHz (12.5 Gbps effective). Despite the A10G's higher effective memory clock, the MI300X's 8192-bit bus width versus 384-bit provides an 8.87x bandwidth advantage. Clock speeds show the A10G with a higher base clock (1320 MHz vs 1000 MHz) but a lower boost clock (1710 MHz vs 2100 MHz), suggesting the MI300X has more thermal headroom for sustained boost.

The MI300X uses a PCIe 5.0 x16 interface, while the A10G uses PCIe 4.0 x16. The A10G has 72 ray tracing cores and 288 tensor cores; the MI300X does not list RT or tensor cores, though its CDNA architecture likely includes matrix accelerators that are not categorized in the same way. The A10G is physically a 267 mm card at 112 mm height, while the MI300X is an OAM module with no listed dimensions.

The Verdict

The data presents a clear hierarchical relationship. The MI300X is a flagship-class accelerator aimed at the highest tier of compute performance, and its 317,994 OpenCL score places it above 100% of all GPUs in the benchmark database. The A10G, with a 97th percentile score, is a capable mid-range server part but is not in the same performance class. For any workload where raw compute throughput or memory capacity is the limiting factor, the MI300X is the only rational choice based on this data.

However, the A10G's 150 W power draw, single-slot form factor, and standard PCIe 4.0 interface make it a practical option for integration into existing server infrastructure. The MI300X requires OAM Module support and a 1150 W PSU recommendation, which limits deployment to systems designed for high-power accelerators. The A10G also retains graphics API support, making it a dual-purpose part for systems that need both compute and display output, though its display outputs are listed as "No outputs" in the fact pack, so this is limited to compute contexts that use the GPU without a physical display.

The benchmark delta of 101.2% in OpenCL is the single most important number on this page. It means the MI300X delivers more than double the performance of the A10G in that test. No amount of power efficiency or form factor convenience can overcome that performance gap for compute-heavy applications. The A10G's nearest rivals, the V100, W6800X, A100, and MI100, all sit within approximately 10% of its score, indicating a competitive mid-range segment. The MI300X's rivals, the H200, L40S, B200, and RTX 6000 Ada, are all within 11% of its score, but the absolute values are nearly double those of the A10G's segment.

For users with existing PCIe-based server infrastructure and modest power budgets, the A10G provides a functional compute solution, especially for workloads under 24 GB. For users building new systems or expanding high-performance compute clusters, the MI300X's 192 GB memory and 5.32 TB/s bandwidth make it a superior choice for large models and data-intensive tasks. The data does not support any scenario where the A10G outperforms the MI300X in raw compute; the only advantages are operational, not performance-based.

FAQ

Q: How much faster is the AMD Instinct MI300X than the NVIDIA A10G in Geekbench OpenCL?

A: The MI300X scores 317,994 while the A10G scores 158,063, giving the AMD part a 101.2% advantage in that benchmark.

Q: What is the memory capacity difference between the two accelerators?

A: The MI300X has 192 GB of HBM3 memory, while the A10G has 24 GB of GDDR6 memory.

Q: Which accelerator supports more graphics APIs?

A: The A10G supports DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4, while the MI300X reports N/A for all graphics APIs.

Q: What is the power consumption difference?

A: The MI300X has a 750 W TDP and a suggested PSU of 1150 W, while the A10G has a 150 W TDP and a suggested PSU of 450 W.

Q: How does the MI300X compare to its nearest rival, the NVIDIA H200 NVL?

A: The MI300X scores 317,994 versus the H200 NVL's 334,891, which is a 5% deficit for the AMD part.

Q: What is the production status of each accelerator?

A: The A10G is listed as end-of-life, while the MI300X has no production status listed.