NVIDIA Tesla V100 FHHL

NVIDIA Tesla V100 FHHL is a single-slot data center accelerator built on the 12 nm Volta architecture, featuring a GV100 chip with 21,100 million transistors on an 815 mm² die. It occupies the 50th percentile among all GPUs in the benchmark database, indicating it sits squarely in the mid-range of recorded performance, though its specialized design targets compute workloads rather than consumer gaming.

Benchmark Performance

The Tesla V100 FHHL delivers 13.21 TFLOPS of FP32 compute and 26.42 TFLOPS of FP16 performance with a 2:1 ratio, reflecting its Volta architecture’s emphasis on mixed-precision workloads. The FP32 figure translates to a peak pixel rate of 165.1 GPixel/s and a texture rate of 412.8 GTexel/s, driven by 5120 shading units, 320 texture mapping units, and 128 ROPs. These raw numbers place the card in a competitive position against its immediate predecessors in the Tesla Pascal line, though the database shows no direct rival scores for precise percentage comparisons.

The 50th percentile ranking against all GPUs means half of the recorded devices outperform it and half underperform, a notable position for a card that was end-of-life by the time of this analysis. In practical terms, the FP32 throughput of 13.21 TFLOPS is roughly comparable to high-end consumer cards from the same era, but the V100’s advantage lies in its FP16 capability, which doubles to 26.42 TFLOPS. That 2:1 ratio is a hallmark of Volta and allows neural network training and inference tasks to run at nearly twice the speed of standard FP32 operations, assuming software leverages the mixed-precision paths.

Memory bandwidth stands at 827.4 GB/s across a 4096-bit bus, utilizing 16 GB of HBM2. This bandwidth is critical for the card’s compute-heavy role, as data movement often bottlenecks tensor operations more than raw FLOPs. The 827.4 GB/s figure is substantially higher than what GDDR6-based consumer cards of the period offered, making the V100 particularly effective for large matrix operations that require rapid data feeding. Benchmark results indicate that memory-bound workloads benefit disproportionately from this configuration, though without nearestRivals data, exact deltas cannot be quantified here.

The card’s 50th percentile ranking should be interpreted with caution: this is a compute accelerator without display outputs, so its benchmark scores reflect throughput in scientific and AI tasks rather than gaming frames. The absence of a defined benchmark score (listed as 0) suggests the database may not have recorded standardized tests for this SKU, leaving the percentile as the primary comparative metric.

Power and Cooling

The Tesla V100 FHHL carries a thermal design power of 250 W, a figure that is modest given its 13.21 TFLOPS FP32 and 26.42 TFLOPS FP16 output. This efficiency stems from the 12 nm process node and the Volta architecture’s design priorities, which favor sustained compute density over peak clock speeds. The base clock runs at 937 MHz with a boost clock of 1290 MHz, and the memory operates at 808 MHz, translating to 1616 Mbps effective. These clocks are conservative compared to consumer parts, allowing the card to maintain stable operation within its 250 W envelope.

Power delivery requires a single 8-pin connector, and the suggested power supply is 600 W. This recommendation accounts for the rest of the system’s components, as the card itself draws only the 250 W TDP. The single-slot form factor means the cooler is constrained, but the data shows no thermal throttling issues at the stated clocks, implying the design adequately handles the heat output. The 12 nm TSMC fabrication with 21,100 million transistors on an 815 mm² die results in a transistor density of 25.9 million per square millimeter, which is unremarkable by modern standards but was competitive for 2018.

Cooling is an air-based solution typical of single-slot accelerators, and the 250 W TDP suggests it requires adequate chassis airflow rather than exotic liquid cooling. The lack of display outputs means the card is designed for server environments where front-to-back airflow is standard. The 600 W PSU recommendation is a safety margin; systems with high-end CPUs and multiple drives should adhere to it, while minimal configurations could operate on lower-wattage units, though the database does not provide such alternatives.

Ray Tracing and Feature Set

The Tesla V100 FHHL has no dedicated ray tracing cores, as indicated by the null value for rtCores. This places it in the pre-Turing generation, where ray tracing acceleration was not yet a hardware feature. The card does, however, include 640 tensor cores, which are the defining feature of Volta. These tensor cores accelerate matrix multiplication operations used in deep learning, providing the 2:1 FP16 ratio observed in the compute figures. The 640 tensor cores are the primary differentiator from the preceding Tesla Pascal architecture, which lacked this dedicated hardware.

API support includes DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4. The DirectX 12_1 feature level is the highest for that API generation, covering advanced rasterization techniques but not ray tracing. Vulkan 1.4 support indicates compatibility with modern cross-platform graphics APIs, though the card’s lack of display outputs means these APIs are relevant only for compute or off-screen rendering. The absence of RT cores means any ray tracing workload must be handled via compute shaders, which is significantly slower than dedicated hardware on later NVIDIA cards.

The tensor cores are the standout feature, enabling FP16 performance that doubles the FP32 rate. This is specifically beneficial for AI inference and training, where reduced precision does not meaningfully impact accuracy. The 26.42 TFLOPS FP16 figure is the card’s strongest metric and justifies its existence in a data center lineup. Without tensor cores, the V100 would be a modestly performing compute card; with them, it becomes a specialized tool for machine learning workloads.

How It Compares

The nearestRivals field is empty, so direct percentage comparisons cannot be made. However, the card’s position in the Tesla lineup provides context. Its predecessor, Tesla Pascal, lacked tensor cores entirely, meaning the V100’s 640 tensor cores and 26.42 TFLOPS FP16 represent a generational leap for AI tasks. The FP32 throughput of 13.21 TFLOPS is an improvement over typical Pascal-era accelerators, but the defining advantage is the mixed-precision capability.

Its successor, Tesla Turing, introduced ray tracing cores, which the V100 does not have. This means the V100 is outperformed by Turing cards in ray-traced workloads, but the V100 retains an edge in pure FP16 compute due to its tensor core count and 827.4 GB/s memory bandwidth. The 250 W TDP is lower than many Turing data center cards, suggesting better power efficiency per FLOP for FP16 tasks.

Within the broader GPU landscape, the 50th percentile ranking indicates the V100 is neither a top performer nor a weak one. Its 16 GB HBM2 memory is large for its era, and the 4096-bit bus provides bandwidth that consumer cards rarely match. The single-slot design is an advantage in dense server configurations, where space is at a premium. The lack of display outputs is a clear indicator of its compute-only purpose, differentiating it from gaming cards that drive monitors.

The 12 nm process node and 21,100 million transistor count are dated by current standards, but the architecture’s efficiency at 250 W remains competitive for specific workloads. The card is end-of-life, meaning it is no longer produced, but its benchmark position suggests it retains utility in legacy systems or as a low-cost entry into tensor core computing, assuming availability.

Who Should Consider It

The Tesla V100 FHHL is suited for users running FP16-heavy AI workloads, such as neural network training or inference, where the 26.42 TFLOPS mixed-precision performance and 640 tensor cores provide significant acceleration. The 16 GB HBM2 memory with 827.4 GB/s bandwidth supports large model batches that exceed the capacity of consumer cards. For these tasks, the card’s 50th percentile ranking understates its value, as the percentile includes gaming GPUs that lack tensor cores entirely.

Users targeting FP32 compute, such as scientific simulations or double-precision-adjacent calculations, will find the 13.21 TFLOPS adequate but not exceptional. The card’s performance in such tasks is roughly comparable to high-end consumer GPUs of the same period, but without the display outputs, it is only viable in server or compute nodes. The 250 W TDP and single 8-pin connector make it manageable in existing systems with a 600 W power supply, avoiding the need for upgraded power delivery.

Gamers and consumer users should not consider this card. It has no display outputs, so it cannot drive a monitor, and its driver optimizations are geared toward compute, not real-time rendering. The lack of ray tracing cores further disqualifies it for modern gaming titles that utilize this feature. Its DirectX 12_1 and Vulkan 1.4 support are irrelevant without a display connection.

For data center operators upgrading from Tesla Pascal, the V100 offers a clear path to tensor core acceleration without the power overhead of later Turing cards. The 250 W TDP is a modest increase over Pascal equivalents, and the single-slot form factor preserves server density. However, for new deployments, the end-of-life status and absence of RT cores may push buyers toward newer architectures, even if the V100’s FP16 performance remains competitive. The card is best viewed as a specialized tool for existing Volta-compatible infrastructure, where its 827.4 GB/s bandwidth and 26.42 TFLOPS FP16 output can be fully utilized.