Comparison

NVIDIA V100 vs NVIDIA A100 80GB

Legacy upgrade path — Volta to Ampere

The A100 80GB delivers 2.5x the FP16 TFLOPS (312 vs 125) with 5x the memory (80GB vs 16GB). The V100 is a legacy option at budget price points.

Pricing Comparison

Specifications

Specification	NVIDIA V100	NVIDIA A100 80GB
Manufacturer	NVIDIA	NVIDIA
Architecture	Volta	Ampere
Accelerator Type	GPU	GPU
Primary Use	training	training
Memory (VRAM)	16 GB	80 GB
FP16 Performance	125 TFLOPS	312 TFLOPS
TDP	300W	400W
Perf per Watt	0.42 TFLOPS/W	0.78 TFLOPS/W

Detailed Analysis

The V100 was the first mainstream data centre GPU with Tensor Cores and pioneered the use of GPUs for deep learning training. The A100 represents a full generational leap.

The performance gap is substantial: 312 vs 125 FP16 TFLOPS, with the A100 also introducing structural sparsity support that can effectively double inference throughput. Memory is where the gap is most dramatic — 80GB HBM2e vs just 16GB HBM2.

The V100 remains available at very low cost points in the cloud. For educational use, experimentation, and lightweight workloads, it can be an economical choice. However, its 16GB memory severely limits the size of models it can handle, making it impractical for modern LLM workloads.

Organisations still running V100s should consider upgrading to A100 or newer for any production workload. The performance and memory improvements more than justify the cost difference.

Verdict

Best for Training

A100 80GB for any serious training. V100 only for experimentation or very small models.

Best for Inference

A100 80GB — the V100's 16GB memory is too limiting for modern models.

Best Value

V100 is cheapest per hour but A100 delivers dramatically better performance per dollar.

Frequently Asked Questions

Is the V100 still usable for AI in 2026?

For lightweight tasks and experimentation, yes. But its 16GB memory makes it impractical for modern large language models. The A100 or newer GPUs are recommended for production workloads.