A10 vs Ampere A100 SXM4
Complete side-by-side comparison of specs, performance, memory, power efficiency, and pricing.
A10
10
Spec Wins
Ampere A100 SXM4
61
Detailed Specifications
A10 — Best For
Ampere A100 SXM4 — Best For
Who Should Choose Each GPU?
Choose A10 if you…
- ✓Need maximum CUDA/TensorRT/vLLM ecosystem compatibility
- ✓Have power-constrained data centers (150W vs 400W TDP)
- ✓Working with a tighter CapEx budget (lower list price)
- ✓Running Inference Serving workloads
- ✓Running Fine-Tuning workloads
- ✓Running Virtual Desktops workloads
Choose Ampere A100 SXM4 if you…
- ✓Need maximum CUDA/TensorRT/vLLM ecosystem compatibility
- ✓Need more VRAM (80GB vs 24GB) for large model inference
- ✓Running Training workloads
- ✓Running Fine-tuning workloads
Verdict
The A10 and Ampere A100 SXM4 target different priorities. The Ampere A100 SXM4's 80GB of HBM2e gives it a clear edge for large-model inference where fitting the full model in VRAM eliminates quantization overhead. Both GPUs use CUDA, so ecosystem switching cost is not a factor. Use our TCO Calculator to model the full 3-year cost difference for your specific utilization and power costs.
A10 vs Ampere A100 SXM4: Common Questions
Which is faster, A10 or Ampere A100 SXM4?+
In FP8 throughput, the Ampere A100 SXM4 leads with 0 TFLOPS vs 0 TFLOPS. For LLM inference, memory capacity and bandwidth often matter more than raw TFLOPS — the Ampere A100 SXM4 has more VRAM (80GB).
Is A10 or Ampere A100 SXM4 better for LLM training?+
For LLM training at scale, the Ampere A100 SXM4 has higher raw throughput. However, the choice also depends on your software stack: A10 offers CUDA compatibility with the widest framework support (PyTorch, JAX, TensorRT).
What is the price difference between A10 and Ampere A100 SXM4?+
The A10 is estimated at $4,000 per unit and the Ampere A100 SXM4 at $12,000. Actual pricing varies by vendor, volume, and configuration. Check our Buy page for current reseller pricing.
Which GPU is more power efficient, A10 or Ampere A100 SXM4?+
The A10 has a lower TDP (150W vs 400W). Performance-per-watt depends on your workload — for FP8 inference, divide TFLOPS by TDP: A10 = 0.0 TFLOPS/W vs Ampere A100 SXM4 = 0.0 TFLOPS/W.