NVIDIA Quadro GV100 vs NVIDIA T1000 Comparison

The NVIDIA T1000 and NVIDIA Quadro GV100 represent two very different approaches to professional graphics, separated by three years of development and targeting distinct workloads. The data shows a clear performance hierarchy, but the smaller, more efficient T1000 still holds relevance for specific use cases. This analysis examines the benchmark results, architectural differences, and what the numbers mean for potential users.

Head-to-Head Benchmarks

The head-to-head comparison is stark, with the Quadro GV100 winning both available tests by a massive margin. In Geekbench OpenCL, the GV100 scores 150,004 points against the T1000’s 37,704, a delta of -74.9% from the T1000’s perspective. The Vulkan test tells a similar story: the GV100 achieves 139,526 versus the T1000’s 34,874, a -75% difference. These are not incremental gains; the GV100 is delivering roughly four times the raw compute throughput in both API tests.

What makes these numbers more striking is that both cards occupy the same 80th percentile among all GPUs in the database. The average benchmark score for the T1000 is 36,289, while the GV100 averages 35,520 — the T1000 actually holds a 2.2% edge in aggregate scores according to its nearest rivals list. This paradox suggests that the Geekbench tests heavily favor the GV100’s massive compute resources, while other untested workloads may level the playing field. The T1000’s nearest rival data shows it performing within 1.2% of the AMD Radeon Pro Duo and 0.7% of the NVIDIA GeForce GTX TITAN X, indicating its 2.5 TFLOPS FP32 output is competitive in its class.

The GV100’s nearest rivals include the NVIDIA GeForce RTX 5070 Ti Mobile at 35,435 (0.2% behind) and the NVIDIA A2 at 34,690 (2.4% behind). This places the GV100 in a different performance tier entirely, one where its 16.66 TFLOPS FP32 and 640 tensor cores dominate the benchmark charts. The 75% deltas in head-to-head tests are not anomalies; they reflect the fundamental gap between a 4 GB workstation card and a 32 GB compute monster.

Where Each One Wins

The data indicates the Quadro GV100 is the clear winner for compute-heavy tasks. Its Geekbench OpenCL score of 150,004 suggests exceptional performance in general-purpose GPU computing, scientific simulation, and rendering workloads that leverage FP32 and FP16 arithmetic. The 640 tensor cores, while not directly benchmarked in the available tests, imply significant advantages in AI inference and deep learning training. The 868.4 GB/s memory bandwidth from its 4096-bit HBM2 interface is crucial for large dataset processing, where the T1000’s 160 GB/s would become a bottleneck.

The T1000 wins in scenarios where power and physical footprint matter more than raw throughput. Its 50 W TDP versus the GV100’s 250 W means it can be deployed in systems with smaller power supplies — the suggested PSU is 250 W versus 600 W. The single-slot design and 156 mm length allow installation in compact chassis where the GV100’s dual-slot, 267 mm form factor would not fit. For basic workstation tasks like 2D CAD, office productivity, and multi-monitor setups, the T1000’s 4x mini-DisplayPort 1.4a outputs provide ample connectivity without the power draw.

However, the benchmark data offers no evidence of the T1000 winning any head-to-head test. Its 0 wins versus 2 for the GV100 is unambiguous. The only comparative advantage is efficiency: the T1000 delivers 2.5 TFLOPS at 50 W (50 GFLOPS/W) versus the GV100’s 16.66 TFLOPS at 250 W (66.6 GFLOPS/W). The GV100 is actually more efficient per watt, but the T1000’s absolute power requirements are far lower, enabling use in environments without dedicated workstation power delivery.

Architecture Differences

The architectural gulf between these two cards is immense. The T1000 uses the TU117 chip on a 12 nm TSMC process, with 4,700 million transistors on a 200 mm² die. The GV100 employs the GV100 chip, also on 12 nm TSMC, but packs 21,100 million transistors into an 815 mm² die — the largest consumer GPU die of its era. Transistor density is similar (23.5M/mm² vs 25.9M/mm²), but the GV100 simply has four and a half times more silicon to work with.

The memory subsystems could not be more different. The T1000 has 4 GB of GDDR6 on a 128-bit bus, yielding 160 GB/s bandwidth. The GV100 offers 32 GB of HBM2 on a 4096-bit bus, delivering 868.4 GB/s — a 5.4x bandwidth advantage and 8x capacity. For datasets exceeding 4 GB, the T1000 simply cannot function, while the GV100 can hold massive models entirely in VRAM.

Compute resources scale accordingly: the T1000 has 896 shading units, 56 TMUs, and 32 ROPs. The GV100 has 5,120 shading units, 320 TMUs, and 128 ROPs. The GV100’s 640 tensor cores have no equivalent in the T1000, which lacks tensor cores entirely. Clock speeds favor the GV100 slightly: base 1132 MHz versus 1065 MHz, boost 1627 MHz versus 1395 MHz. The GV100’s pixel rate of 208.3 GPixel/s is 4.7x the T1000’s 44.64 GPixel/s, and its texture rate of 520.6 GTexel/s dwarfs the T1000’s 78.12 GTexel/s.

FP32 throughput tells the core story: 16.66 TFLOPS versus 2.500 TFLOPS. FP16 follows the same 2:1 ratio pattern: 33.32 TFLOPS versus 5.000 TFLOPS. Both cards support DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4, but the GV100’s compute capabilities extend far beyond graphics APIs into CUDA and tensor-accelerated workloads. The T1000 is Turing architecture, while the GV100 is Volta — the latter designed explicitly for datacenter and AI workloads, the former for entry-level professional graphics.

FAQ

Q: Why does the Quadro GV100 score so much higher in Geekbench tests?

A: The GV100 has 5,120 shading units versus the T1000’s 896, plus 640 tensor cores and 868.4 GB/s memory bandwidth. This translates to 16.66 TFLOPS FP32 versus 2.5 TFLOPS, allowing it to process far more parallel operations per clock cycle.

Q: Is the T1000 competitive with the GV100 in any workload?

A: The benchmark data shows no head-to-head wins for the T1000. However, its 50 W TDP and single-slot design make it suitable for low-power, space-constrained systems where the GV100’s 250 W and dual-slot footprint are prohibitive.

Q: What does the memory difference mean practically?

A: The T1000’s 4 GB GDDR6 limits it to smaller datasets, while the GV100’s 32 GB HBM2 with 868.4 GB/s bandwidth can handle large simulations, high-resolution textures, and AI models without spilling to system memory.

Q: Why do both cards share the same 80th percentile ranking?

A: The percentile reflects average benchmark scores across all GPUs. The T1000’s average of 36,289 is slightly higher than the GV100’s 35,520 due to different test distributions, even though the GV100 dominates the specific Geekbench tests in the head-to-head comparison.

Q: Which card supports newer API features?

A: Both cards support DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4 identically. Neither has ray tracing cores, but the GV100’s tensor cores enable AI features unavailable on the T1000.

Q: Is the GV100 worth its launch MSRP of 8,999 USD?

A: The data shows it delivers approximately 4x the FP32 performance and 5.4x memory bandwidth of the T1000. For users requiring 32 GB VRAM and 16.66 TFLOPS, the GV100’s benchmark scores justify its position in the professional compute market.

The Verdict

The data presents a straightforward conclusion: the Quadro GV100 is the superior performer in every benchmark recorded. Its Geekbench scores are 4x higher, its memory system is in a different class, and its compute resources are an order of magnitude larger. Any workload that can utilize the GV100’s full capabilities — large-scale rendering, scientific computing, AI training — will see enormous benefits over the T1000.

The T1000’s case rests entirely on its physical and power characteristics. At 50 W with no power connectors, it can be installed in systems that cannot accommodate a 250 W card requiring an 8-pin connector. Its 156 mm length fits in small form factor cases, and 4 GB of GDDR6 is adequate for entry-level CAD and 2D workloads. Users with modest performance needs and strict power budgets might find the T1000 sufficient, but they should understand they are sacrificing 74.9% to 75% of benchmark performance compared to the GV100.

For professional users who need maximum compute density and have the power and cooling infrastructure, the GV100 is the only rational choice. Its 5,120 shading units, 640 tensor cores, and 32 GB HBM2 memory make it a versatile compute platform that remains relevant even as newer architectures emerge. The T1000 is a legacy product in its end-of-life phase, while the GV100, also end-of-life, still commands respect for its sheer capability.

Specification Differences

| Specification | NVIDIA T1000 | NVIDIA Quadro GV100 |

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

| Chip | TU117 | GV100 |

| Architecture | Turing | Volta |

| Process Node | 12 nm | 12 nm |

| Transistors | 4,700 million | 21,100 million |

| Die Size | 200 mm² | 815 mm² |

| Base Clock | 1065 MHz | 1132 MHz |

| Boost Clock | 1395 MHz | 1627 MHz |

| Memory Size | 4 GB | 32 GB |

| Memory Type | GDDR6 | HBM2 |

| Memory Bus | 128 bit | 4096 bit |

| Memory Bandwidth | 160.0 GB/s | 868.4 GB/s |

| Shading Units | 896 | 5120 |

| TMUs | 56 | 320 |

| ROPs | 32 | 128 |

| Tensor Cores | None | 640 |

| Pixel Rate | 44.64 GPixel/s | 208.3 GPixel/s |

| Texture Rate | 78.12 GTexel/s | 520.6 GTexel/s |

| FP32 Performance | 2.500 TFLOPS | 16.66 TFLOPS |

| FP16 Performance | 5.000 TFLOPS | 33.32 TFLOPS |

| TDP | 50 W | 250 W |

| Slot Width | Single-slot | Dual-slot |

| Power Connectors | None | 1x 8-pin |

| Suggested PSU | 250 W | 600 W |

| Display Outputs | 4x mini-DisplayPort 1.4a | 4x DisplayPort 1.4a |

| Dimensions | 156 mm x 69 mm | 267 mm x 111 mm |

| Release Date | 2021-05-05 | 2018-03-26 |