AMD FirePro W4300 vs NVIDIA Tesla M2090 Comparison

The Verdict

The data positions these two professional GPUs at different points in the performance and efficiency spectrum. The NVIDIA Tesla M2090 leads in raw compute output, posting a Geekbench OpenCL score of 13,075 against the AMD FirePro W4300's 11,225, a 16.5% advantage. The Tesla M2090 also carries a higher overall percentile ranking, sitting at the 53rd percentile of all GPUs in the database, while the FirePro W4300 sits at the 50th percentile.

However, the choice is not simply about peak compute. The AMD FirePro W4300 delivers its performance at a fraction of the power draw, with a 50 W TDP compared to the Tesla M2090's 250 W. It also offers a full display output suite with four mini-DisplayPort 1.2 connectors, whereas the Tesla M2090 has no display outputs at all. The FirePro W4300 is a single-slot card measuring 171 mm in length, making it suitable for compact workstations. The Tesla M2090 is a dual-slot, 248 mm board requiring both a 6-pin and an 8-pin power connector.

For users who need a compute accelerator with maximum OpenCL throughput and can accommodate a high-power, dual-slot card with no display outputs, the Tesla M2090 is the data-backed pick. For users who need a low-profile, low-power workstation card with native multi-monitor support and modern PCIe 3.0 connectivity, the FirePro W4300 is the logical choice. The recorded data shows a clear trade-off: the Tesla wins on raw score, the FirePro wins on efficiency, physical footprint, and display flexibility.

Architecture Differences

The two cards come from different architectural generations and process nodes. The NVIDIA Tesla M2090 uses the GF110 chip built on Fermi 2.0 architecture, fabricated on a 40 nm process at TSMC. It packs 3,000 million transistors into a 520 mm² die, yielding a transistor density of 5.8 million per square millimeter. The AMD FirePro W4300 uses the Bonaire chip based on GCN 2.0 architecture, also built by TSMC but on a 28 nm process. It contains 2,080 million transistors on a much smaller 160 mm² die, achieving a transistor density of 13.0 million per square millimeter.

The compute resources are configured differently. The Tesla M2090 has 512 shading units, 64 texture mapping units, and 48 raster output pipelines. The FirePro W4300 has 768 shading units, 48 TMUs, and only 16 ROPs. Despite fewer shading units, the Tesla M2090 reaches a peak FP32 throughput of 1,332.2 GFLOPS, while the FirePro W4300 reaches 1,428.5 GFLOPS. The AMD card actually holds a slight theoretical FP32 advantage, yet the benchmark data shows the NVIDIA card winning the OpenCL test, indicating that architecture efficiency and memory subsystem play a decisive role.

Memory configurations differ substantially. The Tesla M2090 comes with 6 GB of GDDR5 on a 384-bit bus, delivering 177.4 GB/s of bandwidth at a 924 MHz memory clock (3.7 Gbps effective). The FirePro W4300 has 4 GB of GDDR5 on a 128-bit bus, providing 96.00 GB/s at a 1500 MHz memory clock (6 Gbps effective). The Tesla has nearly double the memory bandwidth, which helps explain its higher benchmark score.

API support shows a generational gap. Both cards support DirectX 12 and OpenGL 4.6, but the FirePro W4300 supports DirectX 12 (12_0) and Vulkan 1.2.170, while the Tesla M2090 is limited to DirectX 12 (11_0) with no Vulkan support listed. The FirePro also uses PCIe 3.0 x16, while the Tesla is limited to PCIe 2.0 x16. The FirePro W4300 was released on 2015-11-30, while the Tesla M2090 came earlier on 2011-07-24. Both are now end-of-life products.

Head-to-Head Benchmarks

The database records a single head-to-head OpenCL benchmark between these two cards. The NVIDIA Tesla M2090 scores 13,075, while the AMD FirePro W4300 scores 11,225. This gives the Tesla a 16.5% victory in the compute test, and it claims the only win in the head-to-head comparison.

Context from the nearest rivals helps interpret these scores. The Tesla M2090's score of 13,075 places it within 0.7% of the GeForce GTX 1660 SUPER (12,986), 0.9% above the GeForce GTX 950 (13,189, meaning the GTX 950 is actually 0.9% higher), 1% above the RTX 3050 Ti Mobile (12,940), and 1.1% above the Radeon RX 580 (12,928). These deltas are all within a narrow 2% band, indicating the Tesla M2090 performs in line with a cluster of modern consumer GPUs despite its age.

The FirePro W4300's score of 11,225 is essentially identical to the Radeon Pro WX 3200 (11,228, a 0% delta). It sits 0.3% below the GeForce GTX 780M (11,261), and 1.2% above both the RTX PRO 6000 Blackwell Max-Q and RTX PRO 6000D Blackwell Max-Q (both 11,088). The FirePro's rival cluster is consistently lower than the Tesla's, which aligns with the 16.5% gap between the two cards themselves.

The benchmark data also shows that the FirePro W4300's theoretical FP32 peak (1,428.5 GFLOPS) is higher than the Tesla M2090's (1,332.2 GFLOPS), yet the Tesla wins the real-world OpenCL test by 16.5%. This suggests the Tesla's wider memory bus and higher bandwidth (177.4 GB/s vs. 96.00 GB/s) provide a substantial advantage in memory-bound compute workloads. The Tesla also has three times the ROP count (48 vs. 16), which can benefit certain rendering and pixel-heavy tasks.

FAQ

Q: Which GPU has the higher OpenCL benchmark score?

A: The NVIDIA Tesla M2090 scores 13,075 in Geekbench OpenCL, compared to the AMD FirePro W4300's 11,225, a 16.5% difference in favor of the Tesla.

Q: How do the power requirements compare?

A: The Tesla M2090 has a 250 W TDP and requires a 600 W suggested power supply plus one 6-pin and one 8-pin power connector. The FirePro W4300 has a 50 W TDP, requires no power connectors, and only needs a 250 W suggested power supply.

Q: Can both cards output video to displays?

A: No. The Tesla M2090 has no display outputs, making it a compute-only accelerator. The FirePro W4300 has four mini-DisplayPort 1.2 outputs.

Q: What are the memory specifications of each card?

A: The Tesla M2090 has 6 GB of GDDR5 on a 384-bit bus with 177.4 GB/s bandwidth. The FirePro W4300 has 4 GB of GDDR5 on a 128-bit bus with 96.00 GB/s bandwidth.

Q: Which card supports newer PCIe and API standards?

A: The FirePro W4300 uses PCIe 3.0 x16 and supports DirectX 12 (12_0) plus Vulkan 1.2.170. The Tesla M2090 uses PCIe 2.0 x16, supports DirectX 12 (11_0), and has no Vulkan support listed.

Q: How do these cards compare to their nearest rivals in the database?

A: The Tesla M2090 sits within 0.7% to 1.1% of the GeForce GTX 1660 SUPER, GeForce GTX 950, RTX 3050 Ti Mobile, and Radeon RX 580. The FirePro W4300 is within 0.3% to 1.2% of the Radeon Pro WX 3200, GeForce GTX 780M, and two RTX PRO 6000 Blackwell Max-Q variants.

Where Each One Wins

The NVIDIA Tesla M2090 wins in raw compute performance. Its OpenCL score of 13,075 is 16.5% higher than the FirePro W4300's 11,225. It also offers more memory (6 GB vs. 4 GB), a wider memory bus (384-bit vs. 128-bit), and significantly higher memory bandwidth (177.4 GB/s vs. 96.00 GB/s). The Tesla's 48 ROPs triple the FirePro's 16, and its pixel rate of 20.83 GPixel/s exceeds the FirePro's 14.88 GPixel/s. For compute-heavy workloads that rely on memory throughput, the data clearly favors the Tesla. Its performance cluster among rivals, which includes the GeForce GTX 1660 SUPER and Radeon RX 580, shows it remains competitive with much newer consumer cards.

The AMD FirePro W4300 wins in efficiency and practical workstation usability. Its 50 W TDP is one-fifth of the Tesla's 250 W, and it requires no auxiliary power connectors, with a suggested power supply of only 250 W compared to the Tesla's 600 W. The FirePro is a single-slot card at 171 mm length, while the Tesla is dual-slot at 248 mm. The FirePro includes four mini-DisplayPort 1.2 outputs for multi-monitor setups, while the Tesla has no display outputs. The FirePro also offers modern connectivity with PCIe 3.0 x16 and broader API support, including Vulkan 1.2.170 and DirectX 12 (12_0). Its texture rate of 44.64 GTexel/s slightly exceeds the Tesla's 41.66 GTexel/s, and its theoretical FP32 peak of 1,428.5 GFLOPS is marginally higher than the Tesla's 1,332.2 GFLOPS, though this does not translate into a benchmark win.

The use-case split is straightforward. For a dedicated compute node where OpenCL throughput is the priority, power and space are available, and display output is unnecessary, the Tesla M2090 is the stronger option based on a 16.5% benchmark lead. For a compact workstation requiring low power draw, silent single-slot operation, and native multi-monitor support, the FirePro W4300 is the appropriate choice. The database shows two specialized tools: one optimized for raw throughput, the other for efficiency and versatility.