AMD FirePro S10000 vs NVIDIA Tesla M60 Comparison

The AMD FirePro S10000 and NVIDIA Tesla M60 are both end-of-life server accelerators built on the same 28 nm TSMC process, but they represent fundamentally different design philosophies from their respective manufacturers. The data shows a clear, if narrow, overall performance advantage for the AMD part, yet the specifications reveal a more complex story about memory capacity, power efficiency, and architectural priorities that potential users must weigh carefully.

Head-to-Head Benchmarks

The head-to-head benchmark results in the FACT PACK are unambiguous: the AMD FirePro S10000 wins both recorded tests. In Geekbench OpenCL, the AMD card scores 30631 against the Tesla M60's 29506, a 3.8% advantage. The gap widens considerably in the Geekbench Vulkan test, where the FirePro S10000 achieves 34145 compared to 31473 for the M60, an 8.5% lead. This pattern suggests that AMD's GCN 1.0 architecture responds particularly well to the Vulkan API's explicit multi-threading and command buffer model, while NVIDIA's Maxwell 2.0 design shows a smaller improvement over its OpenCL result.

The broader benchmark context reinforces this picture. The FirePro S10000's average benchmark score of 32388 places it at the 77th percentile of all GPUs, while the Tesla M60's 30490 average sits at the 75th percentile. The delta between their averages is approximately 6.2%, consistent with the head-to-head margins. Interestingly, the FirePro S10000's nearest rival is the AMD Radeon RX 7900 GRE with an average score of 32456, a mere 0.2% difference — the S10000 essentially ties a modern desktop GPU in these compute workloads. The Tesla M60's closest competitor is the NVIDIA CMP 70HX at 30476, showing a 0% delta, meaning the M60 is perfectly matched with that mining-focused card.

What the data does not show is any test where the Tesla M60 wins. With winsA at 2 and winsB at 0, the NVIDIA card is consistently behind, though the margins are not catastrophic. The 3.8% OpenCL deficit could be considered within run-to-run variance, but the 8.5% Vulkan gap is more substantive. This raises a question: does the M60's higher theoretical compute throughput (4.825 TFLOPS FP32 versus 3.405 TFLOPS) fail to translate into real-world wins due to architectural inefficiencies, or is the benchmark suite simply not representative of the M60's intended server workloads?

FAQ

Q: Which card has the higher average benchmark score?

A: The AMD FirePro S10000 leads with an average benchmark score of 32388, compared to the NVIDIA Tesla M60's 30490. This places the AMD card at the 77th percentile of all GPUs, two percentage points ahead of the M60's 75th percentile.

Q: How large is the performance gap in the Vulkan benchmark?

A: The AMD FirePro S10000 scores 34145 in Geekbench Vulkan, which is 8.5% higher than the Tesla M60's 31473. This is the largest margin between the two cards in any recorded test.

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

A: The NVIDIA Tesla M60 has 8 GB of GDDR5 memory, while the AMD FirePro S10000 has only 3 GB. However, the AMD card uses a wider 384-bit memory bus, giving it 240.0 GB/s of bandwidth compared to the M60's 160.4 GB/s over a 256-bit bus.

Q: Which card has a higher boost clock speed?

A: The NVIDIA Tesla M60 has a boost clock of 1178 MHz, substantially higher than the AMD FirePro S10000's 950 MHz boost. The base clocks are 557 MHz for the M60 and 825 MHz for the S10000, meaning the AMD card starts higher but the NVIDIA card scales further.

Q: Are both cards the same physical size?

A: No. The AMD FirePro S10000 measures 305 mm in length (12 inches), while the NVIDIA Tesla M60 is shorter at 267 mm (10.5 inches). Both are dual-slot cards.

Q: What is the transistor density difference?

A: The NVIDIA Tesla M60 packs 5,200 million transistors into a 398 mm² die, yielding a density of 13.1M transistors per mm². The AMD FirePro S10000 has 4,313 million transistors on a smaller 352 mm² die, for a density of 12.3M per mm².

Architecture Differences

The architectural divide between these two cards is stark. The AMD FirePro S10000 uses the Tahiti chip with GCN 1.0 architecture, a design that debuted in 2012 and emphasized compute throughput through massive parallelism. It features 1792 shading units, 112 texture mapping units, and 32 ROPs. The NVIDIA Tesla M60, built on the GM204 chip with Maxwell 2.0 architecture, employs 2048 shading units, 128 TMUs, and 64 ROPs — a design that relies on higher clocks and better instruction-level efficiency rather than raw core counts alone.

The clock behavior is telling. The M60's base clock of 557 MHz is remarkably low, but its boost clock of 1178 MHz represents a 111% increase under load. The S10000's base of 825 MHz and boost of 950 MHz show a much more modest 15% boost range. This suggests NVIDIA designed the M60 for aggressive power management, ramping clocks when thermal headroom allows, while AMD's Tahiti runs closer to its sustained limits.

Both cards lack ray tracing cores and tensor cores, as neither architecture predates those technologies. The API support differs subtly: the S10000 supports DirectX 12 (11_1), while the M60 supports DirectX 12 (12_1), indicating a slightly more modern feature level in NVIDIA's implementation. Vulkan support is 1.2.170 for AMD and 1.4 for NVIDIA, a notable generational gap in API version support. Both offer OpenGL 4.6.

The memory architectures are fundamentally different. The S10000's 384-bit bus with 3 GB is optimized for bandwidth over capacity, delivering 240.0 GB/s. The M60's 256-bit bus with 8 GB prioritizes capacity for larger datasets, but its 160.4 GB/s bandwidth is 33% lower. For compute workloads that fit in 3 GB, the AMD card has a clear data movement advantage; for datasets exceeding 3 GB, the NVIDIA card's capacity becomes the deciding factor.

Specification Differences

The specification table reveals several key differences beyond the core architecture. The AMD FirePro S10000 has a TDP of 375 W, while the NVIDIA Tesla M60 draws 300 W — a 75 W difference that favors NVIDIA in power-constrained server environments. The power connector requirements reflect this: the S10000 needs 2x 8-pin connectors, while the M60 requires only 1x 8-pin. The suggested PSU ratings are 750 W for AMD and 700 W for NVIDIA.

Display outputs present a major divergence. The FirePro S10000 includes 1x DVI and 4x mini-DisplayPort 1.2 outputs, making it capable of driving displays directly. The Tesla M60 has no display outputs whatsoever, confirming its role as a pure compute accelerator for headless servers. This difference alone dictates use cases: the S10000 could serve in a workstation or visualization context, while the M60 is strictly for compute farms.

Physical dimensions also differ. The S10000 is longer at 305 mm (12 inches) with a height of 111 mm (4.4 inches), while the M60 is shorter at 267 mm (10.5 inches) with no listed height. Both are dual-slot cards. The transistor counts are 4,313 million for AMD and 5,200 million for NVIDIA, with the M60's larger die size of 398 mm² versus 352 mm² explaining the difference.

Release timing is significant: the S10000 launched on 2012-11-11, while the M60 arrived on 2015-08-29 — nearly three years later. This explains the technological advancements in the NVIDIA card, yet the benchmark data shows AMD's older design still holds a performance edge in these specific tests. The S10000's predecessor is FirePro Terascale and its successor is Radeon Pro GCN; the M60's predecessor is Tesla Kepler and its successor is Tesla Pascal.

Where Each One Wins

The AMD FirePro S10000 wins in every benchmark category where data exists: both OpenCL and Vulkan, with margins of 3.8% and 8.5% respectively. Its higher memory bandwidth of 240.0 GB/s versus 160.4 GB/s suggests it would also excel in bandwidth-bound workloads like dense matrix operations or image processing that fit within its 3 GB frame buffer. The S10000's display outputs make it viable for GPU-accelerated rendering or compute tasks that also require visual output, a capability the M60 lacks entirely.

The NVIDIA Tesla M60 wins on power efficiency, drawing 300 W versus 375 W, a 20% reduction. Its 8 GB memory capacity is more than double the S10000's 3 GB, making it the better choice for workloads with large working sets that exceed 3 GB, such as certain machine learning inference tasks or large-scale data analytics. The M60's higher boost clock of 1178 MHz and greater shading unit count (2048 versus 1792) give it higher theoretical FP32 throughput at 4.825 TFLOPS, even though benchmark scores do not reflect this advantage.

The M60's 64 ROPs versus the S10000's 32 ROPs also point to stronger pixel fill rate performance, though this matters less in compute-focused server workloads. The fact that the M60 supports DirectX 12 (12_1) and Vulkan 1.4 versus the S10000's DirectX 12 (11_1) and Vulkan 1.2.170 means newer graphics features and API optimizations are available on the NVIDIA card, potentially future-proofing it for updated software stacks despite its lower raw benchmark scores.

The Verdict

The data presents a clear choice based on workload priorities. The AMD FirePro S10000 is the higher-performing card in the two benchmark tests recorded, with average scores 6.2% higher and a 77th percentile ranking versus the M60's 75th. Its 240.0 GB/s memory bandwidth and display outputs make it suitable for compute tasks that demand high data throughput and occasional visualization. The 3.8% OpenCL and 8.5% Vulkan wins are decisive, and the card's 3,599 USD launch MSRP reflects its original positioning as a premium compute product.

The NVIDIA Tesla M60, despite losing every benchmark comparison, offers compelling advantages in specific scenarios. Its 8 GB memory capacity is the standout feature, accommodating datasets that would force the S10000 to spill to system memory or fail entirely. The 300 W TDP and single 8-pin connector simplify power delivery in dense server deployments. The newer architecture supports more modern API versions, and the higher theoretical FP32 performance of 4.825 TFLOPS suggests potential in carefully optimized workloads that the Geekbench suite does not capture.

For users prioritizing raw benchmark performance and memory bandwidth, the AMD FirePro S10000 is the data-supported choice. For users with large memory footprints, strict power budgets, or requirements for the latest API features, the NVIDIA Tesla M60 justifies its selection despite lower benchmark scores. The 8.5% Vulkan gap is the most significant performance difference, but in real-world server environments, the M60's 5 GB additional memory capacity could easily outweigh that margin for memory-bound applications. Both cards are end-of-life, so availability and driver support should be verified before deployment.