A100 80GB vs V100 SXM2 32GB
NVIDIA A100 80GB (Ampere, 80 GB) against NVIDIA V100 SXM2 32GB (Volta, 32 GB): memory, compute, power and rental price, compared for LLM inference and training.
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Side-by-Side Specifications
| Spec | A100 80GB | V100 SXM2 32GB |
|---|---|---|
| Architecture | Ampere | Volta |
| Memory | 80 GB HBM2e | 32 GB HBM2 |
| Memory bandwidth | 2,039 GB/s | 900 GB/s |
| FP16 tensor compute | 624 TFLOPS | 125 TFLOPS |
| INT8 tensor compute | 1,248 TOPS | 62.8 TOPS |
| Interconnect | NVLink 3.0 · 600 GB/s | NVLink 2.0 · 300 GB/s |
| TDP | 400 W | 300 W |
| Est. on-demand price | ~$4.50/h | ~$2.00/h |
| FP16 TFLOPS per $/h | 139 | 63 |
Highlighted values indicate the stronger spec. Hourly rates are indicative on-demand estimates.
Verdict
Raw performance: The A100 80GB leads on FP16 tensor compute (5.0x advantage), which translates directly into higher token throughput for inference and shorter training steps.
Memory: With 80 GB per card, the A100 80GB fits larger models on fewer GPUs — fewer cards means less inter-GPU communication and simpler deployments.
Value: At current on-demand rates, the A100 80GB delivers more compute per dollar (139 vs 63 FP16 TFLOPS per $/h). If your model fits in its VRAM budget, it is usually the more economical choice.
GPUs Needed for Popular LLMs
Cards required to serve each model at 8-bit quantization (with 20% overhead for activations and KV cache).
| Model | VRAM (8-bit) | A100 80GB | V100 SXM2 32GB |
|---|---|---|---|
| GPT-5.6 Sol | 2682 GB | 34x | 84x |
| DeepSeek V4 Pro (671B) | 750 GB | 10x | 24x |
| Muse Spark 1.1 | 335 GB | 5x | 11x |
| Claude 5 Sonnet (175B) | 196 GB | 3x | 7x |
| Nova Premier (80B) | 89 GB | 2x | 3x |
| Nova Core (34B) | 38 GB | 1x | 2x |
| Nova Lite (12B) | 13 GB | 1x | 1x |
| Phi 3.5 (3.8B) | 4 GB | 1x | 1x |
Frequently Asked Questions
Which is better for LLM inference: A100 80GB or V100 SXM2 32GB?
The A100 80GB delivers more raw FP16 compute (624 TFLOPS) and the A100 80GB offers the most memory per card (80 GB). For cost-efficiency, the A100 80GB currently gives more FP16 TFLOPS per dollar of on-demand rental (139 vs 63 TFLOPS per $/h).
How much more memory does the A100 80GB have?
The A100 80GB has 80 GB of HBM2e versus 32 GB of HBM2 for the V100 SXM2 32GB — a ratio of 2.50x in favor of the A100 80GB. More VRAM per card means fewer GPUs to fit a given model.
Is the A100 80GB or the V100 SXM2 32GB cheaper to rent?
Estimated on-demand rates are ~$4.50/h for the A100 80GB and ~$2.00/h for the V100 SXM2 32GB. Raw hourly price is only part of the story: normalize by throughput (TFLOPS per $/h) and by how many cards you need for your model's VRAM.
How do the A100 80GB and V100 SXM2 32GB compare on power?
The A100 80GB has a TDP of 400W versus 300W for the V100 SXM2 32GB. FP16 compute per watt: 1.6 vs 0.4 TFLOPS/W.
Deploy on a GPU cloud
Rent the A100 80GB or V100 SXM2 32GB by the hour instead of buying hardware.
