P100 SXM2 vs A10
NVIDIA P100 SXM2 (Pascal, 16 GB) against NVIDIA A10 (Ampere, 24 GB): memory, compute, power and rental price, compared for LLM inference and training.
Pick two GPUs to compare
Side-by-Side Specifications
| Spec | P100 SXM2 | A10 |
|---|---|---|
| Architecture | Pascal | Ampere |
| Memory | 16 GB HBM2 | 24 GB GDDR6 |
| Memory bandwidth | 732 GB/s | 600 GB/s |
| FP16 tensor compute | 21.2 TFLOPS | 250 TFLOPS |
| INT8 tensor compute | 21.2 TOPS | 500 TOPS |
| Interconnect | NVLink 1.0 · 160 GB/s | PCIe 4.0 · 64 GB/s |
| TDP | 300 W | 150 W |
| Est. on-demand price | ~$0.60/h | ~$1.00/h |
| FP16 TFLOPS per $/h | 35 | 250 |
Highlighted values indicate the stronger spec. Hourly rates are indicative on-demand estimates.
Verdict
Raw performance: The A10 leads on FP16 tensor compute (11.8x advantage), which translates directly into higher token throughput for inference and shorter training steps.
Memory: With 24 GB per card, the A10 fits larger models on fewer GPUs — fewer cards means less inter-GPU communication and simpler deployments.
Value: At current on-demand rates, the A10 delivers more compute per dollar (250 vs 35 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) | P100 SXM2 | A10 |
|---|---|---|---|
| GPT-5.6 Sol | 2682 GB | 168x | 112x |
| DeepSeek V4 Pro (671B) | 750 GB | 47x | 32x |
| Muse Spark 1.1 | 335 GB | 21x | 14x |
| Claude 5 Sonnet (175B) | 196 GB | 13x | 9x |
| Nova Premier (80B) | 89 GB | 6x | 4x |
| Nova Core (34B) | 38 GB | 3x | 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: P100 SXM2 or A10?
The A10 delivers more raw FP16 compute (250 TFLOPS) and the A10 offers the most memory per card (24 GB). For cost-efficiency, the A10 currently gives more FP16 TFLOPS per dollar of on-demand rental (250 vs 35 TFLOPS per $/h).
How much more memory does the A10 have?
The P100 SXM2 has 16 GB of HBM2 versus 24 GB of GDDR6 for the A10 — a ratio of 1.50x in favor of the A10. More VRAM per card means fewer GPUs to fit a given model.
Is the P100 SXM2 or the A10 cheaper to rent?
Estimated on-demand rates are ~$0.60/h for the P100 SXM2 and ~$1.00/h for the A10. 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 P100 SXM2 and A10 compare on power?
The P100 SXM2 has a TDP of 300W versus 150W for the A10. FP16 compute per watt: 0.1 vs 1.7 TFLOPS/W.
Deploy on a GPU cloud
Rent the P100 SXM2 or A10 by the hour instead of buying hardware.
