AMD Radeon PRO W6600 vs NVIDIA Tesla T4 Comparison

The AMD Radeon PRO W6600 and NVIDIA Tesla T4 are both end-of-life professional accelerators, but they are built for entirely different worlds. The data shows a clear statistical winner in raw compute, yet the Tesla T4’s architecture tells a story of specialized inference that the benchmark numbers alone cannot capture. The head-to-head results are unambiguous: the Radeon PRO W6600 wins both shared tests, but the T4’s 320 tensor cores and 16 GB frame buffer suggest a purpose that the W6600 does not target.

Head-to-Head Benchmarks

The only two common benchmarks in the data are Geekbench OpenCL and Geekbench Vulkan, and the AMD Radeon PRO W6600 wins both decisively. In OpenCL, the W6600 scores 73,514 against the Tesla T4’s 61,276, a 20% advantage that is substantial in any compute workload. This is not a marginal victory; it is a full generation of raster performance ahead, driven by the W6600’s higher clock speeds and RDNA 2.0 efficiency. In Vulkan, the gap narrows but remains solidly in AMD’s favor: 78,428 versus 72,190, an 8.6% lead. The Vulkan result is particularly telling because it reflects real-time graphics API performance, where the W6600’s 2,580 MHz boost clock and 64 ROPs are directly relevant.

The overall average benchmark score reinforces this trend. The W6600 averages 81,995 across all recorded tests, placing it in the 92nd percentile of all GPUs. The T4 averages 66,733, which still lands in the 90th percentile, but the 15,262-point gap is roughly 22.9% in favor of AMD. When looking at the nearest rivals, the W6600 sits just 1.3% above the AMD Radeon Pro Vega 64X (80,959) and 2.7% above the NVIDIA GeForce RTX 5090 (79,842). The T4, by contrast, is only 1.1% above the AMD Radeon VII (66,004) and 2.5% above the NVIDIA Tesla P40 (65,095). The W6600’s raw score is closer to consumer flagship territory, while the T4 is firmly in the mid-range professional tier.

The deltas tell a deeper story. The W6600’s 20% OpenCL win over the T4 is its largest margin, and it aligns with the W6600’s 9.247 TFLOPS FP32 throughput versus the T4’s 8.141 TFLOPS. The Vulkan win at 8.6% is smaller, suggesting that the T4’s Turing architecture handles certain draw call patterns better than its raw compute would imply, but it still cannot overcome the W6600’s clock advantage. There are no benchmark tests where the T4 wins; the data records 2 wins for AMD and 0 for NVIDIA. This is a clean sweep, but it only covers two API-level tests, leaving the T4’s tensor core workloads entirely unexamined.

Where Each One Wins

The Radeon PRO W6600 is the clear choice for any workload that stresses FP32 shading, texture fill, or pixel output. Its 289.0 GTexel/s texture rate is 13.6% higher than the T4’s 254.4 GTexel/s, and its 165.1 GPixel/s pixel rate is a massive 62.2% higher than the T4’s 101.8 GPixel/s. For CAD viewports, 3D modeling, or any OpenGL-based visualization, the W6600’s 4x DisplayPort 1.4a outputs and higher fill rates make it the practical workstation card. The 8 GB of GDDR6 on a 128-bit bus yields 224.0 GB/s of bandwidth, which is lower than the T4’s 320.0 GB/s, but the W6600 compensates with far higher clocks and a 7 nm process that keeps power at 100 W.

The Tesla T4 wins in capacity and specialization. Its 16 GB of GDDR6 on a 256-bit bus is double the W6600’s memory, and its 320.0 GB/s bandwidth is 42.9% higher. The T4 also has 320 tensor cores, a feature the W6600 lacks entirely. This makes the T4 the logical pick for machine learning inference, particularly in server environments where its 70 W TDP and lack of display outputs are assets rather than liabilities. The T4’s 1,590 MHz boost clock is much lower than the W6600’s 2,580 MHz, but for INT8 or FP16 tensor operations, the T4’s 16.28 TFLOPS FP16 rate is competitive with the W6600’s 18.49 TFLOPS, and the tensor cores can accelerate matrix math that the W6600 must handle at lower efficiency.

Data center deployment favors the T4. It requires no external power connectors, fits in a 168 mm length, and operates within a 250 W suggested PSU, whereas the W6600 needs a 6-pin connector and a 300 W PSU. The T4’s PCIe 3.0 x16 interface offers more lanes than the W6600’s PCIe 4.0 x8, which matters for data transfer in multi-GPU servers. The W6600 is a workstation card with display outputs; the T4 is a headless compute accelerator. Neither is a substitute for the other in their intended roles.

Architecture Differences

The two GPUs come from fundamentally different design philosophies. The Radeon PRO W6600 uses the Navi 23 chip built on RDNA 2.0 architecture, manufactured on TSMC’s 7 nm process. It packs 11,060 million transistors into a 237 mm² die, yielding a transistor density of 46.7 million per square millimeter. The Tesla T4 uses the TU104 chip on NVIDIA’s Turing architecture, built on a 12 nm process. It contains 13,600 million transistors on a much larger 545 mm² die, giving a density of just 25.0 million per square millimeter. The node advantage is stark: AMD achieves 86.8% higher transistor density, which directly explains the W6600’s higher clocks and better performance per watt.

The chip designs diverge sharply in compute resources. The W6600 has 1,792 shading units, 112 TMUs, and 64 ROPs, plus 28 ray tracing cores. The T4 has 2,560 shading units, 160 TMUs, and 64 ROPs, plus 40 ray tracing cores and 320 tensor cores. Despite having 42.9% more shading units, the T4’s lower clock speed (585 MHz base versus 2,331 MHz base) cripples its FP32 throughput. The T4’s 8.141 TFLOPS FP32 is 13.6% below the W6600’s 9.247 TFLOPS. However, the T4’s tensor cores are a unique addition that the W6600 cannot match, enabling hardware-accelerated AI inference workloads that AMD’s card must process through standard shaders.

Memory architecture is another major differentiator. Both use GDDR6, but the T4 has double the capacity (16 GB vs 8 GB) and a wider 256-bit bus versus the W6600’s 128-bit bus. The T4’s 320.0 GB/s bandwidth is 42.9% higher, which is critical for large model weights in inference. The W6600’s 1750 MHz memory clock runs faster than the T4’s 1250 MHz, but the narrower bus limits total throughput. The T4’s base clock of 585 MHz is extraordinarily low, suggesting aggressive power management for server environments, while the W6600’s 2,331 MHz base clock indicates a card designed for sustained desktop rendering.

Specification Differences

The two cards differ on nearly every key specification. The process node is 7 nm for AMD versus 12 nm for NVIDIA. Transistor count is 11,060 million versus 13,600 million. Die size is 237 mm² versus 545 mm². Base clock is 2,331 MHz versus 585 MHz. Boost clock is 2,580 MHz versus 1,590 MHz. Memory size is 8 GB versus 16 GB. Memory bus is 128-bit versus 256-bit. Memory bandwidth is 224.0 GB/s versus 320.0 GB/s. Shading units are 1,792 versus 2,560. TMUs are 112 versus 160. ROPs are identical at 64. Ray tracing cores are 28 versus 40. Tensor cores are absent on the W6600 and 320 on the T4. Pixel rate is 165.1 GPixel/s versus 101.8 GPixel/s. Texture rate is 289.0 GTexel/s versus 254.4 GTexel/s. FP32 is 9.247 TFLOPS versus 8.141 TFLOPS. FP16 is 18.49 TFLOPS versus 16.28 TFLOPS. TDP is 100 W versus 70 W. Power connectors are 1x 6-pin versus none. Suggested PSU is 300 W versus 250 W. Bus interface is PCIe 4.0 x8 versus PCIe 3.0 x16. Display outputs are 4x DisplayPort 1.4a versus none. The W6600 is 241 mm long versus the T4’s 168 mm.

FAQ

Q: Which card has higher raw compute performance in shared benchmarks?

A: The AMD Radeon PRO W6600 wins both shared tests, with a 20% lead in Geekbench OpenCL (73,514 vs 61,276) and an 8.6% lead in Geekbench Vulkan (78,428 vs 72,190).

Q: Does the Tesla T4 have any performance advantage at all?

A: The T4 has higher memory bandwidth (320.0 GB/s vs 224.0 GB/s), double the memory capacity (16 GB vs 8 GB), and features 320 tensor cores that the W6600 does not have, though no benchmark data is available for tensor workloads.

Q: Why does the T4 have more shading units but lower FP32 performance?

A: The T4 has 2,560 shading units versus the W6600’s 1,792, but its base clock of 585 MHz and boost of 1,590 MHz are far below the W6600’s 2,331 MHz base and 2,580 MHz boost, yielding 8.141 TFLOPS versus 9.247 TFLOPS.

Q: Which card is more efficient per watt?

A: The T4 has a 70 W TDP versus the W6600’s 100 W, but the W6600 delivers 9.247 TFLOPS FP32 versus the T4’s 8.141 TFLOPS, meaning the W6600 produces more compute per watt despite drawing more power.

Q: Can the Tesla T4 output video to a display?

A: No. The T4 has no display outputs, while the W6600 has 4x DisplayPort 1.4a connections, making the W6600 suitable for direct workstation use.

Q: What is the release timeline for each card?

A: The W6600 was released on June 7, 2021, with a launch MSRP of 649 USD, while the T4 was released on September 12, 2018, with no recorded launch MSRP.

The Verdict

The data points to a simple conclusion for compute-focused buyers: the AMD Radeon PRO W6600 is the superior card in every measured benchmark. Its 20% OpenCL and 8.6% Vulkan wins are backed by higher clocks, a denser 7 nm process, and better fill rates. The 92nd percentile average score of 81,995 places it above the Tesla T4’s 90th percentile and 66,733 average. Anyone running OpenCL or Vulkan workloads should choose the W6600 without hesitation.

The Tesla T4’s case rests entirely on unmeasured capabilities. Its 16 GB memory and 320 tensor cores target AI inference, and its 70 W TDP with no power connectors makes it ideal for dense server deployments. The 320.0 GB/s bandwidth is 42.9% higher than the W6600, which matters for large datasets. The T4’s 168 mm length and PCIe 3.0 x16 interface also suit multi-GPU racks. For machine learning inference where tensor cores accelerate matrix operations, the T4 is the only credible option, even though no benchmark in the data proves its advantage.

The verdict is clear: the W6600 is the workstation rendering and compute card, the T4 is the headless inference accelerator. Choose the W6600 for any task requiring display output or raw FP32 throughput. Choose the T4 for memory-heavy inference or power-constrained server environments. The benchmark data favors AMD, but the architecture data favors NVIDIA for specialized AI workloads.