AMD Radeon Pro Vega 64X vs NVIDIA Tesla T4 Comparison

The AMD Radeon Pro Vega 64X and NVIDIA Tesla T4 are both end-of-life workstation and server GPUs, but they target completely different use cases. The Vega 64X is an integrated graphics processor (IGP) designed for Apple’s Mac Pro, while the Tesla T4 is a single-slot, low-power accelerator built for data center inference. Benchmark data shows a single head-to-head win for the AMD card, but the raw scores tell only part of the story, as the architectural and power profiles of these two cards diverge sharply.

Where Each One Wins

The AMD Radeon Pro Vega 64X wins the only direct benchmark comparison available. In Geekbench OpenCL, the Vega 64X scores 78,467, while the Tesla T4 scores 61,276, giving AMD a 28.1% advantage. This is the sole head-to-head test in the data, so the AMD card holds a 1-0 win record. The Vega 64X also posts a higher average benchmark score of 80,959 across all its tests, compared to the Tesla T4’s 66,733 average across its own benchmark suite.

The Tesla T4 does not win any head-to-head benchmark in this data set. However, the T4’s benchmark results indicate its strength lies elsewhere. The T4 scores 72,190 in Geekbench Vulkan, which is notably higher than its OpenCL score of 61,276. This suggests the Turing architecture handles Vulkan workloads more efficiently than OpenCL, making it a better fit for applications that leverage Vulkan’s modern API features. The Vega 64X has no Vulkan benchmark score in the data, so its Vulkan performance remains unmeasured.

Looking at percentile rankings, the Vega 64X sits at the 92nd percentile of all GPUs, while the T4 sits at the 90th percentile. This 2-percentile gap reflects the Vega 64X’s higher raw compute throughput. For general compute workloads that scale with shader count and memory bandwidth, the AMD card is the clear winner. For power-constrained server environments or Vulkan-based rendering pipelines, the T4’s lower draw and API support make it the practical choice, despite losing the raw compute comparison.

Architecture Differences

The two cards come from fundamentally different architectural lineages. The Vega 64X uses AMD’s GCN 5.0 architecture on a 14 nm process from GlobalFoundries, featuring the Vega 10 chip. The Tesla T4 uses NVIDIA’s Turing architecture on a 12 nm process from TSMC, built around the TU104 chip. The process node difference is small, but the architectural philosophies diverge significantly.

The Vega 64X packs 4,096 shading units, 256 texture mapping units, and 64 raster operation pipelines. It also carries 12,500 million transistors on a 495 mm² die, yielding a transistor density of 25.3 million per mm². The T4 has 2,560 shading units, 160 TMUs, and 64 ROPs, with 13,600 million transistors on a 545 mm² die, for a density of 25.0 million per mm². Despite the T4 having more transistors, the Vega 64X has 60% more shading units, which explains its higher FP32 throughput.

Memory architecture is another major split. The Vega 64X uses 16 GB of HBM2 on a 2048-bit bus, delivering 512.0 GB/s of bandwidth. The T4 uses 16 GB of GDDR6 on a 256-bit bus, delivering 320.0 GB/s. The Vega 64X’s HBM2 provides 60% more memory bandwidth, which is critical for large data sets and high-resolution compute tasks. The T4’s GDDR6 is more conventional but lower bandwidth, reflecting its focus on inference workloads that often fit in cache or require less memory traffic.

The T4 includes specialized hardware that the Vega 64X lacks: 40 ray tracing cores and 320 tensor cores. These tensor cores are designed for AI inference and matrix operations, giving the T4 a decisive edge in neural network workloads despite its lower raw FP32 throughput. The Vega 64X has no equivalent hardware, so it must rely on general-purpose shader units for all compute tasks.

Head-to-Head Benchmarks

The only direct comparison in the data is Geekbench OpenCL, where the AMD Radeon Pro Vega 64X scores 78,467 against the Tesla T4’s 61,276. This represents a 28.1% advantage for the AMD card. In practical terms, this means the Vega 64X is roughly a quarter faster in OpenCL compute tasks, which covers many scientific, rendering, and video processing workloads.

Breaking down the score, the Vega 64X’s 12.03 TFLOPS of FP32 performance versus the T4’s 8.141 TFLOPS shows a 47.8% raw compute advantage for AMD. The Vega 64X also leads in texture rate with 375.8 GTexel/s versus 254.4 GTexel/s, a 47.7% edge. Pixel rates are closer: the Vega 64X hits 93.95 GPixel/s while the T4 reaches 101.8 GPixel/s, giving NVIDIA a 8.4% lead in this specific metric.

The T4’s FP16 performance is 16.28 TFLOPS (2:1), compared to the Vega 64X’s 24.05 TFLOPS (2:1). Even in FP16, the AMD card leads by 47.7%, but the T4’s tensor cores are designed to accelerate FP16 matrix math far beyond its general-purpose FP16 rate. In AI inference workloads that leverage tensor cores, the T4’s effective throughput would be much higher than its FP16 rating suggests, though the data pack does not provide tensor core benchmark scores.

Considering the nearest rivals, the Vega 64X’s average score of 80,959 places it 1.3% behind the AMD Radeon PRO W6600 (81,995) and 1.4% ahead of the NVIDIA GeForce RTX 5090 (79,842). The T4’s average of 66,733 places it 1.1% ahead of the AMD Radeon VII (66,004) and 2.5% ahead of the NVIDIA Tesla P40 (65,095). These positions confirm that the Vega 64X competes with modern high-end GPUs, while the T4 sits in a lower performance tier.

FAQ

Q: Which card has higher raw compute performance?

A: The AMD Radeon Pro Vega 64X is significantly faster in raw compute. It delivers 12.03 TFLOPS FP32 and 24.05 TFLOPS FP16, compared to the Tesla T4’s 8.141 TFLOPS FP32 and 16.28 TFLOPS FP16. In the Geekbench OpenCL benchmark, the Vega 64X scores 78,467 versus 61,276, a 28.1% advantage.

Q: Does the Tesla T4 have any hardware advantages over the Vega 64X?

A: Yes, the Tesla T4 includes 40 ray tracing cores and 320 tensor cores, which the Vega 64X lacks entirely. These tensor cores are designed for AI inference and matrix operations, giving the T4 a specialized edge in neural network workloads that the Vega 64X cannot match with its general-purpose shader units.

Q: How do their memory systems compare?

A: Both cards have 16 GB of memory, but the Vega 64X uses HBM2 on a 2048-bit bus with 512.0 GB/s bandwidth, while the T4 uses GDDR6 on a 256-bit bus with 320.0 GB/s bandwidth. The Vega 64X provides 60% more memory bandwidth, which benefits large data transfers and high-resolution compute tasks.

Q: What are the power requirements for each card?

A: The Vega 64X has a TDP of 250 W and uses no power connectors, as it is an integrated GPU for the Mac Pro. The Tesla T4 has a TDP of 70 W, uses no power connectors, and requires a suggested PSU of 250 W. The T4 draws 72% less power than the Vega 64X, making it far more efficient for multi-GPU server deployments.

Q: Which card supports newer graphics APIs?

A: The Tesla T4 supports DirectX 12 Ultimate (12_2) and Vulkan 1.4, while the Vega 64X supports DirectX 12 (12_1) and Vulkan 1.3. Both support OpenGL 4.6. The T4’s newer API support enables features like ray tracing and mesh shaders that the Vega 64X cannot utilize.

Q: How do their physical designs differ?

A: The Vega 64X is an integrated GPU (IGP) with no slot width, making it part of a host system rather than a standalone card. The Tesla T4 is a single-slot card measuring 168 mm (6.6 inches) in length, with no display outputs. The T4 is designed for server installation, while the Vega 64X is fixed to a Mac Pro motherboard.

Specification Differences

| Specification | AMD Radeon Pro Vega 64X | NVIDIA Tesla T4 |

|---|---|---|

| Architecture | GCN 5.0 | Turing |

| Process Node | 14 nm | 12 nm |

| Foundry | GlobalFoundries | TSMC |

| Transistors | 12,500 million | 13,600 million |

| Die Size | 495 mm² | 545 mm² |

| Base Clock | 1250 MHz | 585 MHz |

| Boost Clock | 1468 MHz | 1590 MHz |

| Memory Type | HBM2 | GDDR6 |

| Memory Bus Width | 2048 bit | 256 bit |

| Memory Bandwidth | 512.0 GB/s | 320.0 GB/s |

| Shading Units | 4096 | 2560 |

| TMUs | 256 | 160 |

| ROPs | 64 | 64 |

| Ray Tracing Cores | None | 40 |

| Tensor Cores | None | 320 |

| FP32 Performance | 12.03 TFLOPS | 8.141 TFLOPS |

| FP16 Performance | 24.05 TFLOPS | 16.28 TFLOPS |

| Texture Rate | 375.8 GTexel/s | 254.4 GTexel/s |

| Pixel Rate | 93.95 GPixel/s | 101.8 GPixel/s |

| TDP | 250 W | 70 W |

| Slot Width | IGP | Single-slot |

| Length | Not specified | 168 mm (6.6 inches) |

| Display Outputs | Portable Device Dependent | No outputs |

| DirectX Support | 12 (12_1) | 12 Ultimate (12_2) |

| Vulkan Support | 1.3 | 1.4 |

| Release Date | 2019-03-18 | 2018-09-12 |

| Predecessor | Not specified | Tesla Volta |

| Successor | Not specified | Server Ampere |

| Percentile vs All GPUs | 92nd | 90th |

| Average Benchmark Score | 80,959 | 66,733 |

The Verdict

The AMD Radeon Pro Vega 64X is the right choice if your priority is raw compute performance in an integrated, non-upgradeable platform. Its 28.1% lead in OpenCL benchmarks, 47.8% advantage in FP32 throughput, and 60% higher memory bandwidth make it the stronger card for general-purpose compute, scientific simulation, and content creation tasks that rely on shader throughput. The 92nd percentile ranking versus the T4’s 90th confirms its higher tier of performance.

The NVIDIA Tesla T4 is the right choice if you need a low-power, single-slot accelerator for a server environment. Its 70 W TDP versus the Vega 64X’s 250 W makes it dramatically more efficient for multi-GPU deployments, and its 320 tensor cores provide specialized acceleration for AI inference that the Vega 64X cannot offer. The T4’s support for DirectX 12 Ultimate and Vulkan 1.4 adds modern API features, and its 168 mm length fits standard server chassis. The T4’s superior pixel rate of 101.8 GPixel/s versus 93.95 GPixel/s also gives it a small edge in rasterization-heavy tasks.

Choose the Vega 64X for maximum compute density in a Mac Pro. Choose the Tesla T4 for flexible, efficient server deployment with AI inference capabilities. The data clearly favors AMD for raw speed and NVIDIA for efficiency and specialized AI features.