NVIDIA A10M vs NVIDIA Tesla T4 Comparison

The NVIDIA A10M and NVIDIA Tesla T4 are both single-slot, server-oriented accelerators from NVIDIA, but they target very different performance tiers. The A10M is built on the newer Ampere architecture with a GA102 chip, while the T4 uses the older Turing architecture with a TU104 chip. Direct benchmark comparison shows a massive gap: the A10M scores 135,230 in Geekbench OpenCL, while the T4 scores 61,276, a difference of 120.7%. The A10M sits at the 96th percentile among all GPUs, while the T4 is at the 90th percentile. However, the T4 offers a much lower power draw and smaller physical footprint, making it viable for environments where the A10M would be impractical. This analysis breaks down where each card fits based on recorded measurements.

The Verdict

Pick the NVIDIA A10M if your workload demands raw compute throughput and you have the power and space budget. Its OpenCL score of 135,230 is more than double the T4’s 61,276, and it ranks in the 96th percentile of all GPUs, placing it alongside the NVIDIA RTX 4000 Ada Generation (135,218, a 0% difference) and slightly behind the AMD Radeon PRO W6800 (135,396, a 0.1% deficit). The A10M delivers 23.44 TFLOPS of FP32 performance and 23.44 TFLOPS of FP16 with a 1:1 ratio, meaning it does not sacrifice half-precision throughput. Its 20 GB of GDDR6 memory on a 320-bit bus provides 500.2 GB/s of bandwidth, which is critical for large datasets or high-resolution inference. With a 150 W TDP and a suggested 450 W power supply, it is not a low-power part, but it is still manageable in a single-slot form factor.

Pick the NVIDIA Tesla T4 if your priority is power efficiency and compact size. The T4 runs on a 70 W TDP with no power connectors required, and its suggested power supply is only 250 W. Its physical length is 168 mm (6.6 inches), significantly shorter than the A10M’s 267 mm (10.5 inches). This makes it suitable for dense server chassis or edge deployments where space and thermals are constrained. Its OpenCL score of 61,276 and Vulkan score of 72,190 place it at the 90th percentile, which is still respectable. It outperforms the AMD Radeon VII (66,004, a 1.1% margin) and the NVIDIA Tesla P40 (65,095, a 2.5% margin) in average benchmark score. However, its FP32 throughput is only 8.141 TFLOPS, and FP16 is 16.28 TFLOPS with a 2:1 ratio, meaning half-precision work runs faster but at the cost of reduced FP32 capability.

Do not choose the T4 for heavy compute or training tasks. The data shows a 120.7% deficit in OpenCL performance, and its memory bandwidth of 320.0 GB/s is 36% lower than the A10M’s 500.2 GB/s. Choose the A10M for anything that stresses FP32 or unified FP16/FP32 workloads, and choose the T4 only when the 70 W power envelope or short card length is non-negotiable.

FAQ

Q: Which card has a higher benchmark score?

A: The NVIDIA A10M scores 135,230 in Geekbench OpenCL, while the NVIDIA Tesla T4 scores 61,276 in the same test. The A10M also has a Vulkan score of 72,190 (only the T4 has a recorded Vulkan score), but the OpenCL comparison shows the A10M leading by 120.7%.

Q: How do these cards compare in power consumption?

A: The A10M has a 150 W TDP and requires an 8-pin EPS power connector, with a suggested 450 W power supply. The T4 has a 70 W TDP, requires no power connectors, and has a suggested 250 W power supply. The T4 draws less than half the power of the A10M.

Q: What are the memory differences?

A: The A10M has 20 GB of GDDR6 memory with a 320-bit bus and 500.2 GB/s bandwidth. The T4 has 16 GB of GDDR6 memory with a 256-bit bus and 320.0 GB/s bandwidth. The A10M offers 25% more capacity and 56% more bandwidth.

Q: Are these cards still in production?

A: Both are marked as end-of-life in the database. The T4 has a release date of September 12, 2018, while the A10M has no recorded release date. The T4’s predecessor is Tesla Volta, and its successor is Server Ampere. The A10M’s predecessor is Tesla Turing, and its successor is Server Ada.

Q: Which card has more shading units and tensor cores?

A: The A10M has 7,168 shading units, 224 texture mapping units, 80 render output units, 56 ray tracing cores, and 224 tensor cores. The T4 has 2,560 shading units, 160 TMUs, 64 ROPs, 40 ray tracing cores, and 320 tensor cores. The A10M has more shading units, but the T4 has more tensor cores (320 vs. 224).

Q: What is the physical size difference?

A: The A10M is 267 mm (10.5 inches) long and 112 mm (4.4 inches) high. The T4 is 168 mm (6.6 inches) long, with no recorded height. Both are single-slot cards and have no display outputs.

Architecture Differences

The A10M uses the GA102 chip on the Ampere architecture, fabricated on an 8 nm process at Samsung. It contains 28,300 million transistors on a 628 mm² die, giving a transistor density of 45.1 million per mm². The T4 uses the TU104 chip on the Turing architecture, fabricated on a 12 nm process at TSMC. It contains 13,600 million transistors on a 545 mm² die, with a transistor density of 25.0 million per mm². The A10M’s newer process allows for more than double the transistor count in a slightly larger die, which explains its higher compute density.

The A10M’s FP32 throughput is 23.44 TFLOPS, and its FP16 throughput is also 23.44 TFLOPS with a 1:1 ratio, meaning the card does not halve its rate for half-precision. The T4’s FP32 is 8.141 TFLOPS, but its FP16 is 16.28 TFLOPS with a 2:1 ratio, meaning it doubles its throughput for half-precision work. This architectural difference is significant: the A10M is designed for workloads that need consistent FP32 and FP16, while the T4 prioritizes FP16 for inference tasks.

The A10M has 56 ray tracing cores, while the T4 has 40. The A10M also has 224 tensor cores, but the T4 has 320 tensor cores, which is a notable reversal. Despite having fewer tensor cores, the A10M’s higher overall compute capability means it still outperforms the T4 in the recorded OpenCL benchmark. The transistor density difference (45.1 vs. 25.0 million per mm²) reflects the A10M’s more advanced manufacturing node, which allows for more features per area.

Both cards support DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4, so API-level features are identical. The A10M uses PCIe 4.0 x16, while the T4 uses PCIe 3.0 x16, giving the A10M double the theoretical bus bandwidth, though this only matters for data transfer, not raw compute.

Specification Differences

The two cards differ in nearly every major specification. The A10M has 7,168 shading units, 224 TMUs, and 80 ROPs, versus the T4’s 2,560 shading units, 160 TMUs, and 64 ROPs. The A10M’s pixel rate is 130.8 GPixel/s, and its texture rate is 366.2 GTexel/s, while the T4’s pixel rate is 101.8 GPixel/s and texture rate is 254.4 GTexel/s. These differences result in the A10M being roughly 28% faster in pixel fill and 44% faster in texture fill.

Clock speeds also differ. The A10M has a base clock of 975 MHz and a boost clock of 1635 MHz. The T4 has a base clock of 585 MHz and a boost clock of 1590 MHz. Despite a lower base clock, the T4’s boost clock is only 45 MHz lower than the A10M’s, but the A10M’s much higher core count (7,168 vs. 2,560) is the primary driver of its performance advantage.

Memory specifications are distinct as well. The A10M has 20 GB of GDDR6 on a 320-bit bus, with a memory clock of 1563 MHz (12.5 Gbps effective) and bandwidth of 500.2 GB/s. The T4 has 16 GB of GDDR6 on a 256-bit bus, with a memory clock of 1250 MHz (10 Gbps effective) and bandwidth of 320.0 GB/s. The A10M’s memory subsystem is clearly superior in both capacity and speed.

Power and physical specs diverge sharply. The A10M has a 150 W TDP, requires an 8-pin EPS connector, and a suggested 450 W power supply. The T4 has a 70 W TDP, no power connectors, and a suggested 250 W power supply. The A10M is 267 mm long and 112 mm high, while the T4 is 168 mm long with no recorded height. Both are single-slot and have no display outputs.

Head-to-Head Benchmarks

The only direct benchmark recorded in the database is Geekbench OpenCL. The A10M scores 135,230, and the T4 scores 61,276, giving the A10M a win with a 120.7% performance advantage. This is not a marginal difference; it is a complete blowout. The A10M’s score places it at the 96th percentile of all GPUs, while the T4’s score places it at the 90th percentile. In terms of nearest rivals, the A10M is essentially tied with the NVIDIA RTX 4000 Ada Generation (135,218, a 0% difference) and the AMD Radeon PRO W6800 (135,396, a 0.1% difference). The T4, by contrast, sits above the AMD Radeon VII (66,004, a 1.1% margin) and the NVIDIA Tesla P40 (65,095, a 2.5% margin), but below the AMD Radeon Instinct MI25 (68,562, a 2.7% deficit) and the Intel Arc A770 (68,809, a 3% deficit).

The A10M also has a higher average benchmark score of 135,230, while the T4’s average benchmark score is 66,733, which includes its Vulkan score of 72,190 alongside the OpenCL score of 61,276. Even if the T4’s best score (Vulkan at 72,190) is considered, it still falls far short of the A10M’s OpenCL result. The delta of 120.7% in OpenCL is the only head-to-head metric, and it clearly favors the A10M.

Where Each One Wins

The A10M wins in every compute-heavy scenario. Its FP32 throughput of 23.44 TFLOPS is nearly triple the T4’s 8.141 TFLOPS, and its FP16 throughput is 23.44 TFLOPS versus the T4’s 16.28 TFLOPS. For workloads like scientific simulation, 3D rendering, or any task that relies on FP32, the A10M is the clear choice. Its 20 GB memory capacity and 500.2 GB/s bandwidth also make it better suited for large batch sizes in machine learning inference or training, where the T4’s 16 GB and 320.0 GB/s could become a bottleneck. The A10M’s higher pixel rate (130.8 vs. 101.8 GPixel/s) and texture rate (366.2 vs. 254.4 GTexel/s) further cement its advantage in graphics-oriented tasks, even though both cards lack display outputs.

The T4 wins in scenarios where power and size are the limiting factors. Its 70 W TDP is less than half of the A10M’s 150 W, and it requires no external power connectors, making it easier to deploy in existing servers without power cabling. Its 168 mm length is 99 mm shorter than the A10M, which is critical in half-height or short-depth chassis. The T4’s higher tensor core count (320 vs. 224) suggests it was optimized for inference workloads that use tensor operations, and its FP16 performance (16.28 TFLOPS) is respectable, though still lower than the A10M’s FP16 output. For edge inference, media transcoding, or low-power inference servers, the T4’s efficiency makes it a practical choice despite its lower raw performance.

The data also shows the T4 has a Vulkan score of 72,190, which is not available for the A10M in the database. This means the T4 has been validated in Vulkan workloads, but no direct comparison can be made. In terms of percentile, the A10M’s 96th percentile versus the T4’s 90th percentile indicates that the A10M is closer to the top of the GPU hierarchy, while the T4 is still above average but not in the same class. Ultimately, the A10M is for performance-first deployments, and the T4 is for power-constrained or space-constrained deployments.