A10 vs T4
NVIDIA A10 (Ampere, 24 GB) against NVIDIA T4 (Turing, 16 GB): memory, compute, power and rental price, compared for LLM inference and training.
Pick two GPUs to compare
Side-by-Side Specifications
| Spec | A10 | T4 |
|---|---|---|
| Architecture | Ampere | Turing |
| Memory | 24 GB GDDR6 | 16 GB GDDR6 |
| Memory bandwidth | 600 GB/s | 320 GB/s |
| FP16 tensor compute | 250 TFLOPS | 65 TFLOPS |
| INT8 tensor compute | 500 TOPS | 130 TOPS |
| Interconnect | PCIe 4.0 · 64 GB/s | PCIe 3.0 · 32 GB/s |
| TDP | 150 W | 70 W |
| Est. on-demand price | ~$1.00/h | ~$0.50/h |
| FP16 TFLOPS per $/h | 250 | 130 |
Highlighted values indicate the stronger spec. Hourly rates are indicative on-demand estimates.
Verdict
Raw performance: The A10 leads on FP16 tensor compute (3.8x 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 130 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) | A10 | T4 |
|---|---|---|---|
| GPT-5.6 Sol | 2682 GB | 112x | 168x |
| DeepSeek V4 Pro (671B) | 750 GB | 32x | 47x |
| Muse Spark 1.1 | 335 GB | 14x | 21x |
| Claude 5 Sonnet (175B) | 196 GB | 9x | 13x |
| Nova Premier (80B) | 89 GB | 4x | 6x |
| Nova Core (34B) | 38 GB | 2x | 3x |
| 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: A10 or T4?
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 130 TFLOPS per $/h).
How much more memory does the A10 have?
The A10 has 24 GB of GDDR6 versus 16 GB of GDDR6 for the T4 — a ratio of 1.50x in favor of the A10. More VRAM per card means fewer GPUs to fit a given model.
Is the A10 or the T4 cheaper to rent?
Estimated on-demand rates are ~$1.00/h for the A10 and ~$0.50/h for the T4. 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 A10 and T4 compare on power?
The A10 has a TDP of 150W versus 70W for the T4. FP16 compute per watt: 1.7 vs 0.9 TFLOPS/W.
Deploy on a GPU cloud
Rent the A10 or T4 by the hour instead of buying hardware.
