AMD Radeon RX 7900M vs NVIDIA A10M Comparison

The NVIDIA A10M and AMD Radeon RX 7900M represent two fundamentally different interpretations of high-performance graphics, yet the benchmark data places them in a surprisingly close contest. The A10M is a server-focused Ampere part, while the RX 7900M is a mobile RDNA 3.0 flagship. The single shared benchmark, Geekbench OpenCL, shows the A10M scoring 135,230 against the RX 7900M’s 129,499, a margin of 4.4% in NVIDIA’s favor. That is a meaningful but hardly dominant lead, suggesting that for raw compute throughput in this specific test, the two are near-peers. The RX 7900M, however, counters with a Geekbench Vulkan score of 158,760, a result that does not have a direct A10M counterpart in the data. The A10M’s average benchmark score of 135,230 places it in the 96th percentile of all GPUs, while the RX 7900M’s average of 97,487 (dragged down by the inclusion of the 3DMark Steel Nomad result) sits in the 94th percentile. The 3DMark Steel Nomad DX12 score of 4,201 for the RX 7900M is a modern rasterization workload, and its absence for the A10M hints that the NVIDIA part is not optimized for or targeted at that kind of gaming-centric test.

Head-to-Head Benchmarks

The only direct comparison available is Geekbench OpenCL, a compute-oriented test that stresses raw floating-point and memory throughput. The NVIDIA A10M achieves 135,230 points, while the AMD Radeon RX 7900M trails with 129,499 points, yielding a 4.4% advantage for the A10M. This is a narrow win, but it is consistent with the A10M’s positioning among its nearest rivals. The A10M’s closest competitor in the data is the NVIDIA RTX 4000 Ada Generation, which scores 135,218, a delta of 0%. The AMD Radeon PRO W6800 scores 135,396, putting it 0.1% ahead of the A10M, and the Radeon Pro W6800X Duo is 0.4% ahead at 135,774. The A10M is thus not an outlier; it sits squarely in a cluster of high-end workstation cards, all within a 1% band. The RX 7900M, by contrast, is matched against a different tier in its nearest rivals list. Its average score of 97,487 is 0.4% above the AMD Radeon Pro VII (97,131), 4.3% below the NVIDIA Quadro RTX 6000 (101,872), and 5.4% above the AMD Radeon Instinct MI60 (92,466). This suggests that while the RX 7900M’s OpenCL score is close to the A10M’s, its overall average benchmark performance is dragged down by the 3DMark Steel Nomad result, which is a specific DX12 workload that may not be representative of compute-oriented tasks.

The 4.4% delta in OpenCL is worth interrogating. The A10M’s architecture is built for server workloads, with 7,168 shading units and 224 tensor cores, while the RX 7900M has 4,608 shading units but a higher boost clock of 2,090 MHz versus the A10M’s 1,635 MHz. The A10M’s FP32 throughput is listed at 23.44 TFLOPS, while the RX 7900M reaches 38.52 TFLOPS. Despite the RX 7900M’s higher theoretical FP32 rate, it loses in the OpenCL test. This implies that the A10M’s memory subsystem, with a 320-bit bus and 500.2 GB/s bandwidth, is better utilized in this workload than the RX 7900M’s 256-bit bus and 576.0 GB/s bandwidth, or that the driver optimizations for OpenCL on NVIDIA hardware are superior. The Vulkan score for the RX 7900M, 158,760, is significantly higher than its OpenCL score, indicating that the RDNA 3.0 architecture is more efficient in that API. Without a Vulkan score for the A10M, the data cannot confirm whether the NVIDIA part would maintain its lead there.

The Verdict

The data points to a clear split in use cases. For compute-heavy OpenCL workloads, the NVIDIA A10M is the stronger choice, with a 4.4% lead over the RX 7900M and a 96th percentile ranking among all GPUs. Its nearest rivals are all within a 0.9% band, which suggests that the A10M is a stable, predictable performer in that niche. The RX 7900M, while only 4.4% behind in OpenCL, shows its strength in Vulkan with a score of 158,760, which is 22.6% higher than its own OpenCL result. For applications that leverage Vulkan, the RX 7900M is likely to be more responsive. The 3DMark Steel Nomad score of 4,201 for the RX 7900M, while not directly comparable, indicates that it can handle modern DX12 titles, a workload that the A10M, with no display outputs, is not designed to address. The A10M’s server pedigree—single-slot form factor, 8-pin EPS power connector, 150 W TDP—makes it a fit for rack-mounted compute nodes, whereas the RX 7900M’s IGP form factor and "Portable Device Dependent" display outputs mark it as a mobile part for laptops. The A10M is end-of-life, while the RX 7900M is active and was released on 2023-10-18. A user needing a current, Vulkan-capable mobile GPU should choose the RX 7900M; a user needing a compute-focused, server-deployable accelerator should choose the A10M based on its OpenCL edge.

FAQ

Q: Which GPU wins in Geekbench OpenCL, and by how much?

A: The NVIDIA A10M wins with 135,230 points versus the AMD Radeon RX 7900M’s 129,499 points, a 4.4% advantage.

Q: Does the RX 7900M have any benchmark where it clearly outperforms the A10M?

A: The RX 7900M scores 158,760 in Geekbench Vulkan, which is higher than its own OpenCL score, but there is no Vulkan score for the A10M in the data to compare directly.

Q: How does the A10M compare to its closest rivals in the benchmark database?

A: The A10M’s score of 135,230 is 0% different from the NVIDIA RTX 4000 Ada Generation (135,218), 0.1% below the AMD Radeon PRO W6800 (135,396), and 0.4% below the AMD Radeon Pro W6800X Duo (135,774).

Q: What is the RX 7900M’s average benchmark score and percentile rank?

A: The RX 7900M has an average benchmark score of 97,487 and sits in the 94th percentile of all GPUs. Its nearest rival, the AMD Radeon Pro VII, scores 97,131, which is 0.4% lower.

Q: Which GPU has a higher FP32 compute throughput?

A: The AMD Radeon RX 7900M has a higher FP32 throughput at 38.52 TFLOPS, while the NVIDIA A10M is rated at 23.44 TFLOPS.

Q: Are both GPUs the same physical size?

A: No. The NVIDIA A10M is a single-slot card measuring 267 mm in length and 112 mm in height, while the RX 7900M is an IGP with no listed dimensions.

Specification Differences

The two GPUs diverge sharply on core specifications. The A10M is built on an 8 nm process by Samsung, packing 28,300 million transistors into a 628 mm² die, for a transistor density of 45.1M per mm². The RX 7900M uses TSMC’s 5 nm process, with 57,700 million transistors on a 529 mm² die, achieving a density of 109.1M per mm². The A10M has more shading units (7,168 versus 4,608) and more tensor cores (224 versus none listed for AMD), but the RX 7900M has more TMUs (288 versus 224) and ROPs (192 versus 80). Memory configurations also differ: the A10M has 20 GB of GDDR6 on a 320-bit bus with 500.2 GB/s bandwidth, while the RX 7900M has 16 GB on a 256-bit bus with 576.0 GB/s bandwidth. Clock speeds favor AMD, with a base of 1,825 MHz and boost of 2,090 MHz, against NVIDIA’s 975 MHz base and 1,635 MHz boost. The A10M’s TDP is 150 W, while the RX 7900M draws 180 W. Physically, the A10M is a single-slot card with an 8-pin EPS power connector and a suggested 450 W PSU, while the RX 7900M is an IGP with no power connectors listed. The A10M offers no display outputs, while the RX 7900M’s outputs are "Portable Device Dependent."

Architecture Differences

Architecturally, these are generations apart. The A10M is based on the GA102 chip under NVIDIA’s Ampere architecture, belonging to the Server Ampere (Axx) generation. It includes 56 RT cores and 224 tensor cores, supporting DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. The RX 7900M uses the Navi 31 chip with RDNA 3.0 architecture, codenamed Plum Bonito, from the Navi Mobile (RX 7000M) generation. It has 72 RT cores and no tensor cores listed, but it supports the same API set: DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. The process node difference is significant: 8 nm Samsung versus 5 nm TSMC, leading to the RX 7900M’s higher transistor density (109.1M per mm² versus 45.1M per mm²). The A10M’s FP16 throughput is 23.44 TFLOPS (1:1 ratio with FP32), while the RX 7900M achieves 77.05 TFLOPS FP16 (2:1 ratio), indicating a stronger half-precision focus on the AMD part. The A10M’s predecessor is Tesla Turing, and its successor is Server Ada, while the RX 7900M’s predecessor is Polaris Mobile with no listed successor. The A10M is end-of-life, whereas the RX 7900M is active, with a release date of 2023-10-18.

Where Each One Wins

The NVIDIA A10M wins in the only head-to-head benchmark available—Geekbench OpenCL—by 4.4%. This is its home turf: a compute-centric test where its 20 GB memory buffer and 500.2 GB/s bandwidth can be fully leveraged. Its 96th percentile rank and tight clustering with rivals like the RTX 4000 Ada Generation (0% delta) and Radeon PRO W6800 (0.1% delta) indicate predictable, professional-grade compute performance. The A10M also wins on memory capacity (20 GB versus 16 GB) and has tensor cores, which are absent from the RX 7900M’s spec sheet, making it a better fit for AI inference workloads that rely on those cores. Its lower TDP (150 W versus 180 W) and single-slot form factor are advantages for dense server deployments where space and power are constrained.

The AMD Radeon RX 7900M wins on raw compute throughput, with 38.52 TFLOPS FP32 versus the A10M’s 23.44 TFLOPS, and its Vulkan score of 158,760 is 22.6% higher than its own OpenCL score, suggesting that API-optimized workloads will run faster. Its higher boost clock (2,090 MHz versus 1,635 MHz) and larger ROP count (192 versus 80) point to better rasterization performance, which the 3DMark Steel Nomad score of 4,201 supports. The RX 7900M also wins on memory bandwidth (576.0 GB/s versus 500.2 GB/s) despite a narrower 256-bit bus, and its 5 nm process gives it a transistor density advantage (109.1M per mm² versus 45.1M per mm²) that implies better power efficiency per transistor. For mobile users needing a current-generation GPU with display output support and Vulkan capabilities, the RX 7900M is the only choice, as the A10M has no display outputs and is end-of-life. The RX 7900M’s FP16 throughput of 77.05 TFLOPS is over three times the A10M’s 23.44 TFLOPS, making it the stronger option for half-precision compute tasks.