NVIDIA A2 vs NVIDIA T1000 Comparison

The Verdict

The data presents a clear but nuanced picture for these two NVIDIA workstation cards. The NVIDIA T1000, a Turing-generation part, wins both head-to-head benchmark comparisons included in the database. It scores 37,704 in Geekbench OpenCL against the A2's 35,357, a 6.6% advantage, and 34,874 in Geekbench Vulkan against the A2's 34,023, a 2.5% edge. The T1000 also holds a higher average benchmark score of 36,289 versus 34,690 for the A2, placing it in the 80th percentile of all GPUs compared to the A2's 79th.

However, the A2 is not a straightforward loss. Its architecture and memory configuration serve a fundamentally different purpose. The A2 has 16 GB of GDDR6 memory—four times the T1000's 4 GB—and a larger memory bandwidth of 200.1 GB/s compared to 160.0 GB/s. It also brings hardware features the T1000 lacks entirely: 10 RT cores and 40 tensor cores. The A2's higher FP32 throughput of 4.531 TFLOPS versus 2.500 TFLOPS for the T1000 indicates raw compute headroom that the benchmark suite does not fully capture.

The verdict depends on workload. For general-purpose compute and graphics tasks measured by standard benchmark suites, the T1000 is the stronger performer. For memory-intensive AI inference, large dataset processing, or any workload that leverages tensor cores and RT cores, the A2 is the only viable choice between these two. The T1000 is end-of-life, as is the A2, but the A2's feature set aligns with modern accelerated computing demands.

Where Each One Wins

The T1000 wins in the two benchmark categories recorded: Geekbench OpenCL and Geekbench Vulkan. Its OpenCL lead of 6.6% over the A2 is the largest margin in the head-to-head data. This suggests the T1000's Turing architecture, with its higher texture rate of 78.12 GTexel/s versus 70.80 GTexel/s for the A2, translates to better performance in compute workloads that rely on texture operations. The T1000 also has more TMUs—56 versus 40—which supports this interpretation.

The T1000's pixel rate of 44.64 GPixel/s is lower than the A2's 56.64 GPixel/s, but that does not appear to hurt it in the recorded benchmarks. The T1000's advantage in Vulkan, though smaller at 2.5%, still indicates a consistent lead in graphics API performance. The T1000 also has display outputs—four mini-DisplayPort 1.4a connectors—while the A2 has none. For any workstation requiring direct display connectivity, the T1000 is the sole option.

The A2 wins in memory capacity and bandwidth. With 16 GB versus 4 GB, it can hold substantially larger models and datasets in VRAM. Its 200.1 GB/s bandwidth is 25% higher than the T1000's 160.0 GB/s. The A2's FP32 throughput of 4.531 TFLOPS is 81% higher than the T1000's 2.500 TFLOPS. These are not benchmark wins recorded in the database, but they are architectural wins that matter for specific workloads. The A2 also supports PCIe 4.0 x8, while the T1000 uses PCIe 3.0 x16; the newer bus standard offers higher bandwidth per lane.

Architecture Differences

The T1000 is built on the TU117 chip using Turing architecture on a 12 nm TSMC process. It contains 4,700 million transistors on a 200 mm² die, yielding a transistor density of 23.5 million per mm². The A2 uses the GA107 chip with Ampere architecture on an 8 nm Samsung process. It packs 8,700 million transistors on the same 200 mm² die size, achieving 43.5 million transistors per mm²—nearly double the density.

The A2's Ampere architecture introduces hardware features absent from the T1000's Turing design. The A2 has 10 RT cores for ray tracing and 40 tensor cores for AI acceleration. The T1000 has neither. This is the most significant architectural divergence. The A2's FP16 performance is 4.531 TFLOPS at a 1:1 ratio with FP32, meaning it does not sacrifice throughput for half-precision. The T1000 achieves 5.000 TFLOPS FP16 but at a 2:1 ratio, indicating it uses a different execution path that halves the rate.

Memory configurations differ sharply. The T1000 has 4 GB GDDR6 on a 128-bit bus at 1250 MHz (10 Gbps effective), yielding 160.0 GB/s. The A2 has 16 GB GDDR6 on the same 128-bit bus but at 1563 MHz (12.5 Gbps effective), producing 200.1 GB/s. Both use 32 ROPs, but the T1000 has 56 TMUs versus the A2's 40, and the A2 has 1280 shading units against the T1000's 896.

The A2's clock speeds are higher: 1440 MHz base and 1770 MHz boost versus 1065 MHz base and 1395 MHz boost for the T1000. The A2 supports DirectX 12 Ultimate (12_2), while the T1000 supports DirectX 12 (12_1). Both support OpenGL 4.6 and Vulkan 1.4. The A2 has no display outputs, while the T1000 offers four mini-DisplayPort 1.4a connectors. Power consumption is modest for both: 50 W for the T1000 and 60 W for the A2, each requiring a 250 W suggested PSU.

FAQ

Q: Which card has better raw compute performance?

A: The NVIDIA A2 has higher FP32 throughput at 4.531 TFLOPS compared to the T1000's 2.500 TFLOPS. The A2 also has more shading units (1280 versus 896) and higher boost clocks (1770 MHz versus 1395 MHz).

Q: Why does the T1000 win the recorded benchmarks if the A2 has higher specs?

A: The T1000 scores 37,704 in Geekbench OpenCL and 34,874 in Vulkan, beating the A2's 35,357 and 34,023 respectively. The T1000 has a higher texture rate (78.12 GTexel/s versus 70.80 GTexel/s) and more TMUs (56 versus 40), which likely benefits these specific workloads.

Q: Can the A2 drive displays?

A: No. The A2 has no display outputs. The T1000 has four mini-DisplayPort 1.4a connectors, making it the only option for direct monitor connectivity.

Q: Which card is better for AI and ray tracing workloads?

A: The A2 is the only option with dedicated hardware for these tasks. It has 10 RT cores and 40 tensor cores, while the T1000 has none. The A2's 16 GB memory also provides more capacity for AI models.

Q: How do their memory systems compare?

A: The A2 has 16 GB GDDR6 with 200.1 GB/s bandwidth. The T1000 has 4 GB GDDR6 with 160.0 GB/s bandwidth. The A2's memory operates at 1563 MHz (12.5 Gbps effective) versus 1250 MHz (10 Gbps effective) for the T1000.

Q: Are both cards the same physical size?

A: The T1000 measures 156 mm in length and 69 mm in height. The A2's dimensions are not listed in the data. Both are single-slot cards with no power connectors required.

Head-to-Head Benchmarks

The Geekbench OpenCL test shows the largest gap between these two cards. The T1000 scores 37,704 against the A2's 35,357, a 6.6% delta. This is a decisive win for the Turing card. The T1000's average benchmark score of 36,289 places it within 0.7% of the AMD Radeon RX 5300M and the NVIDIA GeForce GTX TITAN X, both scoring around 36,529–36,530. The A2's average of 34,690 sits within 0.4% of the NVIDIA T1000 8 GB and AMD Radeon HD 7970.

In Geekbench Vulkan, the T1000 wins again but by a narrower margin: 34,874 versus 34,023, a 2.5% delta. This smaller gap suggests the A2's Ampere architecture handles Vulkan workloads more competitively than OpenCL, though it still falls short. The T1000's Vulkan score is closer to its own OpenCL score (34,874 versus 37,704), while the A2's scores are nearly identical across both APIs (34,023 versus 35,357).

The T1000's nearest rival, the AMD Radeon Pro Duo, trails by 1.2%, and the NVIDIA Quadro GV100 trails by 2.2%. The A2's closest rival, the NVIDIA T1000 8 GB, is just 0.4% behind, and the NVIDIA TITAN V is 1% behind. These proximity values indicate that both cards sit in a tightly contested performance band.

The wins tally is 2–0 in favor of the T1000. Neither benchmark shows the A2 ahead. Yet the A2's architectural advantages—tensor cores, RT cores, 16 GB memory, higher FP32, and PCIe 4.0—are not reflected in these two tests. The data suggests that for the workloads these benchmarks represent, the T1000's Turing design with higher texture throughput and TMU count provides better outcomes. The A2's strengths lie in workloads that the recorded tests do not measure, particularly those that can exploit its tensor and RT hardware or require large memory capacity.