AMD FirePro D300 vs NVIDIA Tesla K40m Comparison
AMD FirePro D300
Tesla K40m
PERFORMANCE BENCHMARKS
Analysis: AMD FirePro D300 vs NVIDIA Tesla K40m
The NVIDIA Tesla K40m and AMD FirePro D300 are both end-of-life professional workstation cards from the 2013–2014 era, built on the same 28 nm TSMC process. Despite their shared vintage, they target different segments of the compute market. Benchmark data from Geekbench shows the Tesla K40m leading in the single head-to-head OpenCL test, but the FirePro D300 counters with a Vulkan score and a more balanced profile. The data indicates that the Tesla K40m is the raw compute specialist, while the FirePro D300 is a display-capable workhorse for visual workloads.
Where Each One Wins
The primary split between these two cards is compute capability versus display functionality. The NVIDIA Tesla K40m is a pure accelerator with no display outputs, designed for server racks and compute clusters where rendering to a screen is unnecessary. Its benchmark results reflect this focus: it achieves a Geekbench OpenCL score of 19,885, which places it in the 65th percentile of all GPUs. The FirePro D300, by contrast, offers 4x DisplayPort 1.2 outputs, making it suitable for multi-monitor professional visualization, CAD, or video editing environments. Its Geekbench OpenCL score of 19,515 is only 1.9% lower, but it also posts a Geekbench Vulkan score of 19,759, a workload the Tesla cannot run.
In terms of pure throughput, the Tesla wins on every computational metric. It delivers 5.046 TFLOPS of FP32 performance versus 2.176 TFLOPS for the FirePro, a 2.3x advantage. The Tesla also leads in pixel rate (52.56 GPixel/s vs 27.20 GPixel/s) and texture rate (210.2 GTexel/s vs 68.00 GTexel/s). However, the FirePro D300 wins on power efficiency and physical footprint. It consumes 150 W TDP versus 245 W for the Tesla, and occupies a single slot versus the Tesla’s dual-slot design. For a workstation with multiple GPUs, the FirePro’s lower power draw and smaller size are significant practical advantages.
Architecture Differences
The architectural gap is substantial. The Tesla K40m uses the GK110B chip, based on NVIDIA’s Kepler architecture, manufactured on a 28 nm process at TSMC. The chip contains 7,080 million transistors on a 561 mm² die, yielding a transistor density of 12.6M / mm². The FirePro D300 uses the Pitcairn chip, based on AMD’s GCN 1.0 architecture, also on 28 nm TSMC process. Pitcairn packs 2,800 million transistors on a 212 mm² die, with a higher transistor density of 13.2M / mm². The Kepler design favors larger, more complex compute units, while GCN 1.0 is built around simpler, more numerous stream processors.
The Tesla’s compute configuration is far larger: 2,880 shading units, 240 texture mapping units, and 48 raster operation units. The FirePro D300 has 1,280 shading units, 80 TMUs, and 32 ROPs. This difference explains the Tesla’s superior throughput numbers. Memory architecture also diverges. The Tesla uses a 384-bit bus with 12 GB of GDDR5 memory, providing 288.4 GB/s of bandwidth. The FirePro uses a 256-bit bus with 2 GB of GDDR5, offering 162.6 GB/s. The Tesla’s memory capacity is 6x larger, which is critical for datasets that exceed 2 GB.
Both cards support DirectX 12 (11_1) and OpenGL 4.6. The Tesla supports Vulkan 1.2.175, while the FirePro supports Vulkan 1.2.170, a minor version difference. Neither has dedicated ray tracing or tensor cores. Clock behavior differs too: the Tesla has defined base (745 MHz) and boost (876 MHz) clocks, while the FirePro D300 has no base or boost clock listed in the data. The FirePro’s memory runs at 1270 MHz (5.1 Gbps effective), versus the Tesla’s 1502 MHz (6 Gbps effective).
Head-to-Head Benchmarks
The only direct head-to-head benchmark available is Geekbench OpenCL, where the NVIDIA Tesla K40m scores 19,885 against the AMD FirePro D300’s 19,515. This yields a 1.9% victory for the Tesla. The margin is modest, suggesting that in OpenCL compute tasks, the two cards are closer than their raw specifications would imply. The Tesla’s higher FP32 throughput should theoretically produce a larger gap, but the benchmark results indicate that the FirePro’s GCN architecture is efficient at extracting performance from its smaller configuration.
In the broader context of nearest rivals, the Tesla’s average score of 19,885 puts it 0.1% behind the AMD FirePro W7000 (19,905) and 0.6% ahead of the AMD Radeon RX 6650 XT (19,765). The FirePro D300’s average score of 19,637 is 1.3% behind the Tesla, and it sits 0.2% ahead of the NVIDIA Quadro K5200 (19,602). The FirePro D300 also has a Geekbench Vulkan score of 19,759, which is 1.2% higher than its own OpenCL score and 0.6% below the Radeon RX 6650 XT’s average. Notably, the FirePro D300 is listed as a nearest rival to the Tesla, with a deltaPct of 1.3% (meaning the Tesla is 1.3% faster), while the Tesla is listed as a rival to the FirePro with a deltaPct of -1.2% (meaning the FirePro is 1.2% slower).
These numbers suggest that in real-world OpenCL workloads, neither card has a decisive edge. The Tesla’s 1.9% win is within typical run-to-run variance. However, for tasks that can leverage Vulkan, the FirePro D300 has an additional capability that the Tesla lacks entirely. The data shows the Tesla wins the single compute benchmark, but the FirePro offers a more versatile software stack.
Specification Differences
The two cards differ in nearly every measurable specification. Memory capacity is the most dramatic: 12 GB on the Tesla versus 2 GB on the FirePro. Memory bandwidth follows, with the Tesla at 288.4 GB/s versus 162.6 GB/s. The memory bus width is 384-bit on the Tesla versus 256-bit on the FirePro. Shading units are 2,880 vs 1,280, TMUs are 240 vs 80, and ROPs are 48 vs 32. FP32 compute is 5.046 TFLOPS vs 2.176 TFLOPS. Pixel rate is 52.56 GPixel/s vs 27.20 GPixel/s. Texture rate is 210.2 GTexel/s vs 68.00 GTexel/s.
Transistor count is 7,080 million vs 2,800 million, and die size is 561 mm² vs 212 mm². Transistor density is 12.6M / mm² vs 13.2M / mm². TDP is 245 W vs 150 W. The Tesla is dual-slot, the FirePro is single-slot. The Tesla has no display outputs, the FirePro has 4x DisplayPort 1.2. The Tesla’s base clock is 745 MHz with a boost of 876 MHz; the FirePro has no base or boost clock listed. Memory clock is 1502 MHz (6 Gbps effective) on the Tesla versus 1270 MHz (5.1 Gbps effective) on the FirePro. The Tesla is 267 mm long (10.5 inches), the FirePro is 242 mm (9.5 inches).
The Tesla’s suggested PSU is 550 W, the FirePro’s is 450 W. The Tesla has a launch MSRP of 7,699 USD; the FirePro has no launch MSRP listed. Release dates differ: the Tesla launched on 2013-11-21, the FirePro on 2014-01-17. The Tesla’s predecessor is Tesla Fermi and successor is Tesla Maxwell. The FirePro’s predecessor is FirePro Terascale and successor is Radeon Instinct. Vulkan support is 1.2.175 on the Tesla versus 1.2.170 on the FirePro. DirectX and OpenGL versions are identical (12 (11_1) and 4.6).
FAQ
Q: Which card has more memory, and why does it matter?
A: The NVIDIA Tesla K40m has 12 GB of GDDR5 memory, while the AMD FirePro D300 has 2 GB. The 6x larger capacity allows the Tesla to hold much larger datasets in VRAM, which is critical for scientific computing, deep learning inference, or large-scale rendering tasks that exceed 2 GB.
Q: Can the Tesla K40m drive a display?
A: No. The Tesla K40m has no display outputs. It is a compute-only accelerator designed for server environments. The FirePro D300, in contrast, has 4x DisplayPort 1.2 outputs, making it suitable for multi-monitor professional workstations.
Q: How do their OpenCL scores compare?
A: The Tesla K40m scores 19,885 in Geekbench OpenCL, while the FirePro D300 scores 19,515. The Tesla leads by 1.9% in this head-to-head test. Both cards are within 1.4% of each other in the nearest rival list, indicating similar overall OpenCL performance.
Q: Which card has better Vulkan support?
A: The FirePro D300 supports Vulkan 1.2.170 and has a Geekbench Vulkan score of 19,759. The Tesla K40m supports Vulkan 1.2.175 but has no Vulkan benchmark result in the data. The FirePro’s Vulkan score is slightly higher than its OpenCL score, suggesting the API is well-optimized on that card.
Q: What is the power consumption difference?
A: The Tesla K40m has a TDP of 245 W and a suggested PSU of 550 W, while the FirePro D300 has a TDP of 150 W and a suggested PSU of 450 W. The FirePro draws 95 W less, which can be significant in multi-GPU systems for thermal and power budget management.
Q: Are these cards still relevant for modern workloads?
A: Both are end-of-life products. The Tesla K40m sits in the 65th percentile of all GPUs, and the FirePro D300 in the 64th percentile. Their OpenCL scores are competitive with newer mid-range cards like the AMD Radeon RX 6650 XT (19,765), but they lack modern features like ray tracing or tensor cores.