GPU Comparison

The comparison between the AMD Radeon Pro W6900X and the NVIDIA A10M reveals two very different design philosophies: one aimed at professional visualization on Apple platforms, the other at dense server deployment. The data shows the A10M wins the only shared benchmark, but the W6900X counters with a significantly larger memory pool and a much higher average benchmark score across its test suite.

FAQ

Q: Which GPU has the higher average benchmark score?

A: The AMD Radeon Pro W6900X has a substantially higher average benchmark score of 168,574, compared to 135,230 for the NVIDIA A10M.

Q: How do the two compare in the Geekbench OpenCL test?

A: The NVIDIA A10M wins the head-to-head Geekbench OpenCL test with a score of 135,230 against the AMD Radeon Pro W6900X’s 130,035, a delta of -3.8% for the AMD card.

Q: What is the difference in memory capacity between the two cards?

A: The AMD Radeon Pro W6900X features 32 GB of GDDR6 memory, while the NVIDIA A10M is equipped with 20 GB of GDDR6 memory. The AMD card has a 12 GB capacity advantage.

Q: Which card has a higher transistor count and larger die size?

A: The NVIDIA A10M has more transistors at 28,300 million on a 628 mm² die, while the AMD Radeon Pro W6900X has 26,800 million transistors on a smaller 520 mm² die.

Q: What are the peak FP32 performance figures for each card?

A: The NVIDIA A10M leads slightly in raw FP32 compute with 23.44 TFLOPS, while the AMD Radeon Pro W6900X delivers 22.23 TFLOPS.

Q: What interface does each card use to connect to the system?

A: The AMD Radeon Pro W6900X uses an Apple MPX bus interface, whereas the NVIDIA A10M uses a standard PCIe 4.0 x16 interface.

Architecture Differences

The architectural split between these two GPUs is stark, reflecting their divergent target environments. The AMD Radeon Pro W6900X is built on the Navi 21 chip using the RDNA 2.0 architecture, fabricated on a 7 nm process at TSMC. This results in a die size of 520 mm² containing 26,800 million transistors, yielding a transistor density of 51.5M per mm². In contrast, the NVIDIA A10M uses the GA102 chip with the Ampere architecture, manufactured on an 8 nm process at Samsung, with a larger 628 mm² die and a higher transistor count of 28,300 million, but a lower density of 45.1M per mm².

The compute core layouts differ significantly. The AMD card packs 5,120 shading units, 320 TMUs, and 128 ROPs, along with 80 dedicated ray tracing cores. The NVIDIA card counters with more shading units at 7,168, but fewer TMUs at 224 and fewer ROPs at 80. The A10M also includes 56 RT cores and a notable 224 tensor cores, a feature entirely absent from the AMD specification. This indicates a design leaning towards AI and compute workloads on the NVIDIA side, while AMD’s configuration focuses on rasterization throughput.

Clock speeds and power characteristics tell another part of the story. The W6900X operates at a base clock of 1825 MHz and boosts to 2171 MHz, while the A10M runs much slower at a 975 MHz base and 1635 MHz boost. However, the A10M draws only 150 W of power with a suggested 450 W PSU, whereas the W6900X has a 300 W TDP and requires a 700 W suggested PSU. The NVIDIA card is a single-slot design with an 8-pin EPS power connector, while the AMD card’s slot width is not specified. The A10M also has no display outputs, being a server-oriented part, while the W6900X provides 1x HDMI 2.1 and 4x Thunderbolt outputs for Mac Pro workstations.

Head-to-Head Benchmarks

The only directly comparable benchmark in the data is Geekbench OpenCL, and the results show a narrow victory for the NVIDIA A10M. The A10M scores 135,230, while the AMD Radeon Pro W6900X trails with 130,035, a difference of -3.8%. This is a modest lead for the NVIDIA card, suggesting that in raw OpenCL compute, the two are closely matched despite their different architectures and power envelopes.

The NVIDIA card’s advantage here likely stems from its higher peak FP32 throughput of 23.44 TFLOPS versus the AMD’s 22.23 TFLOPS, a difference of roughly 5.4%. The A10M’s higher shading unit count of 7,168 also contributes to this edge. However, the AMD card’s higher boost clock of 2171 MHz helps it close the gap, as do its superior pixel and texture rates. The W6900X achieves 277.9 GPixel/s and 694.7 GTexel/s, far exceeding the A10M’s 130.8 GPixel/s and 366.2 GTexel/s. This suggests that in scenarios heavily reliant on rasterization throughput, the AMD card would outperform, even though the single OpenCL test shows the NVIDIA card ahead.

Beyond this single test, the overall benchmark picture diverges. The AMD Radeon Pro W6900X has an average benchmark score of 168,574, placing it at the 97th percentile among all GPUs. Its nearest rivals include the NVIDIA RTX 4500 Ada Generation at 166,094 (1.5% slower), the NVIDIA RTX A5500 at 165,217 (2% slower), and the AMD Radeon PRO W7800 at 164,894 (2.2% slower). The NVIDIA A10M, by contrast, has an average score of just 135,230, placing it at the 96th percentile. Its closest competitors are the NVIDIA RTX 4000 Ada Generation at 135,218 (0% difference), the AMD Radeon PRO W6800 at 135,396 (-0.1%), and the AMD Radeon Pro W6800X Duo at 135,774 (-0.4%). This places the A10M in a completely different performance tier, roughly 24.6% behind the W6900X in average score.

Specification Differences

The two cards differ across nearly every major specification category, starting with the process node and foundry. The AMD Radeon Pro W6900X uses a 7 nm TSMC process, while the NVIDIA A10M uses an 8 nm Samsung process. The die sizes differ as well, with the AMD at 520 mm² and the NVIDIA at 628 mm².

Memory is a major point of differentiation. The W6900X offers 32 GB of GDDR6 on a 256-bit bus with a bandwidth of 512.0 GB/s and effective memory speed of 16 Gbps. The A10M has 20 GB of GDDR6 on a wider 320-bit bus, but its bandwidth is slightly lower at 500.2 GB/s, with effective memory speed of 12.5 Gbps. The AMD card thus has both more capacity and marginally higher bandwidth.

Clock speeds show a significant divergence, with the AMD card running at 1825 MHz base and 2171 MHz boost, versus the NVIDIA’s 975 MHz base and 1635 MHz boost. Memory clocks also differ, with AMD at 2000 MHz and NVIDIA at 1563 MHz. The compute metrics reflect these differences: AMD has 5120 shading units, 320 TMUs, and 128 ROPs, while NVIDIA has 7168 shading units, 224 TMUs, and 80 ROPs. The AMD card has 80 RT cores, while the NVIDIA has 56 RT cores and 224 tensor cores.

Power and physical specifications differ dramatically. The W6900X has a 300 W TDP and a suggested PSU of 700 W, while the A10M is much more efficient at 150 W TDP and a 450 W suggested PSU. The A10M is single-slot with an 8-pin EPS connector, while the W6900X’s slot width is unspecified and it uses an Apple MPX interface. The W6900X has display outputs (1x HDMI 2.1, 4x Thunderbolt), while the A10M has none. The AMD card is 267 mm long and 120 mm high, while the NVIDIA card is 267 mm long and 112 mm high. The W6900X was released on 2021-08-02 with a launch MSRP of 5,999 USD, while the A10M has no release date or MSRP listed and lists Tesla Turing as its predecessor and Server Ada as its successor.

Where Each One Wins

The AMD Radeon Pro W6900X wins decisively in scenarios requiring large memory footprints and high rasterization throughput. Its 32 GB of GDDR6 memory is 60% larger than the A10M’s 20 GB, making it better suited for massive datasets in rendering, video editing, or complex scene compositing where the extra capacity prevents spills to system memory. Its pixel rate of 277.9 GPixel/s is more than double the A10M’s 130.8 GPixel/s, and its texture rate of 694.7 GTexel/s nearly doubles the NVIDIA’s 366.2 GTexel/s. For workloads like 3D modeling viewports, high-resolution texture streaming, or GPU-accelerated compositing, the W6900X’s architecture provides clear advantages. Its display outputs also make it a viable option for direct workstation use, whereas the A10M cannot drive a monitor at all.

The NVIDIA A10M wins in the efficiency and compute-specific domains. Its 150 W TDP is exactly half the AMD card’s 300 W, allowing for denser server deployments with lower cooling requirements. The presence of 224 tensor cores gives it a dedicated hardware path for AI inference and training tasks that the AMD card cannot match. Its slightly higher FP32 throughput of 23.44 TFLOPS also gives it a marginal edge in general compute. The A10M’s single-slot form factor and PCIe 4.0 x16 interface make it far easier to integrate into existing server infrastructure, and its 8-pin EPS power connector is a standard server component. The A10M also wins the single head-to-head benchmark, the Geekbench OpenCL test, by 3.8%.

The Verdict

The data presents a clear split between two professional GPU use cases. For a workstation environment, particularly within the Apple ecosystem, the AMD Radeon Pro W6900X is the stronger choice. Its 32 GB memory capacity, higher pixel and texture rates, and display outputs make it a comprehensive solution for content creation and visualization. The average benchmark score of 168,574 places it at the 97th percentile, significantly ahead of the A10M’s 135,230. The W6900X’s 2.2% advantage over the AMD Radeon PRO W7800 and 3.7% lead over the NVIDIA A100 PCIe 40 GB in the rival list further cements its position as a high-end performer.

For a server or datacenter environment, the NVIDIA A10M is the more appropriate pick, despite its lower raw performance. The 150 W TDP and single-slot design allow for much higher density per rack, and the 224 tensor cores make it a purpose-built tool for AI workloads. Its 96th percentile ranking is still strong, and its near-identical score to the NVIDIA RTX 4000 Ada Generation shows it competes well within its own tier. The A10M’s lack of display outputs is irrelevant in a headless server, and its PCIe 4.0 x16 interface is a standard, flexible connection. Ultimately, the choice comes down to whether the priority is maximum memory and rendering throughput (W6900X) or maximum efficiency and tensor core capability (A10M). The benchmark data confirms the W6900X is the faster card overall, but the A10M wins the specific battle where efficiency and AI features are paramount.