V100 32GB
Previous-gen workhorse. 32GB HBM2 at low cost. Still widely available. No FP8 or BF16 hardware support.
V100 32GB Overview
The NVIDIA V100 32GB is a Volta-generation data center GPU that was the flagship AI accelerator from 2018 to 2020, before the A100 superseded it. Built on the GV100 die (TSMC 12nm), it delivers 112 TFLOPS of FP16 throughput with 32GB of HBM2 at 900 GB/s bandwidth. It was the first GPU to introduce Tensor Cores for accelerated matrix multiplication, establishing the hardware foundation for modern deep learning training.
The 32GB HBM2 configuration fits models up to approximately 16B parameters at FP16. NVLink 2.0 at 300 GB/s bidirectional enables multi-GPU tensor parallelism, though at lower bandwidth than the A100's NVLink 3.0 (600 GB/s) or H100's NVLink 4.0 (900 GB/s). The V100 lacks BF16 and FP8 hardware support — both introduced in later architectures — which limits its efficiency for modern transformer training compared to A100 and H100.
The V100 32GB is best suited for legacy ML training workloads, HPC applications, and budget-conscious teams that need more than 16GB of VRAM at the lowest possible cost. It remains widely available on AWS (p3 instances) and other providers at significantly lower rates than A100. For new workloads, the A100 is almost always the better choice — but for teams with existing V100-optimized code or tight budgets, it remains a viable option.
Memory
Compute Performance
Hardware
Relative Performance
Relative to highest-spec GPU in database
Limitations
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Use Case Guidance
LLM Model Size Guidance
Estimates only. Actual capacity depends on context length, KV cache, and framework overhead.
Related Guides
LLM APIs Running on This GPU Class
Providers that serve frontier LLM inference on Volta-class hardware.
Related GPUs
Frequently Asked Questions
How much VRAM does the V100 32GB have?
The V100 32GB has 32GB of HBM2 memory with 900 GB/s bandwidth. This enables running models up to approximately 64B parameters at INT4 precision, 32B at INT8, or 16B at FP16.
What is the FP16 performance of the V100 32GB?
The V100 32GB delivers 112 TFLOPS of FP16 performance and 112 TFLOPS BF16. INT8 throughput is 224 TOPS. For transformer inference, memory bandwidth (900 GB/s) is often the binding constraint rather than raw TFLOPS.
What is the V100 32GB best used for?
The V100 32GB is best suited for: Legacy ML training, HPC workloads, Budget multi-GPU setups. Previous-gen workhorse. 32GB HBM2 at low cost. Still widely available. No FP8 or BF16 hardware support.
What interconnect does the V100 32GB use?
The V100 32GB uses NVLink 2.0 / PCIe 3.0 with 300 GB/s NVLink bandwidth for multi-GPU configurations. NVLink enables near-linear tensor-parallel scaling across multiple cards for models that exceed single-card VRAM.
What LLM model sizes can the V100 32GB run?
With 32GB of HBM2, the V100 32GB can run models up to approximately 16B parameters at FP16 (2 bytes/param), 32B at INT8 (1 byte/param), or 64B at INT4/GGUF (0.5 bytes/param). These are estimates — actual capacity depends on context length, KV cache size, and framework overhead. Longer context windows require more KV cache memory, reducing the effective model size that fits.
How does the V100 32GB compare to the A100 for LLM inference?
The V100 32GB has 112 TFLOPS FP16 vs the A100 80GB's 312 TFLOPS, and 900 GB/s memory bandwidth vs the A100's 2,039 GB/s. For memory-bound autoregressive LLM inference, bandwidth is the primary determinant of tokens-per-second. The A100's higher bandwidth gives it a throughput advantage for large model inference, despite the V100 32GB's lower cost.
What is the power consumption of the V100 32GB?
The V100 32GB has a TDP (Thermal Design Power) of 300W. This is the maximum sustained power draw under full load. For data center deployments, total rack power consumption is typically 1.2–1.5× the GPU TDP when accounting for CPU, memory, networking, and cooling overhead. At 300W, the V100 32GB is in the mid-range tier — compatible with standard data center power infrastructure.
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