GPU × use-case guide · Translation
Is the A100 40GB SXM a good GPU for translation?
The A100 40GB SXM is a ampere NVIDIA GPU with 40 GB HBM2e (1555 GB/s), 312 TFLOPS BF16 / 312 TFLOPS FP8, and a 400 W TDP. Translation workloads care most about high batch throughput and cost efficiency across many short-to-medium requests. Here's how the A100 40GB SXM measures up.
What models fit on a single A100 40GB SXM?
Weights only, reserving ~25% of the 40 GB for KV cache, activations and fragmentation. ✓ = fits on one card.
| Model | BF16 | FP8 | INT4 |
|---|---|---|---|
| Llama 3.1 8B | ✓ | ✓ | ✓ |
| Qwen 2.5 14B | ✓ | ✓ | ✓ |
| Gemma 2 27B | — | ✓ | ✓ |
| Mixtral 8x7B (MoE) | — | — | ✓ |
| Llama 3.3 70B | — | — | — |
| Qwen 2.5 72B | — | — | — |
| Llama 3.1 405B | — | — | — |
Largest single-card fit: Qwen 2.5 14B at BF16, Gemma 2 27B at FP8, Mixtral 8x7B (MoE) at INT4. Bigger models need tensor-parallel across 8 cards.
A100 40GB SXM for translation, specifically
Translation is throughput-bound rather than VRAM-bound, so the A100 40GB SXM's 1555 GB/s of bandwidth and 312 TFLOPS matter more than raw capacity. Because it runs offline, batch aggressively to push tokens-per-dollar down. Size it precisely on the calculator.
A100 40GB SXM pricing across providers
| Provider | On-demand $/hr | Reserved $/hr |
|---|---|---|
| tensordock | $1.19 | — |
| lambda | $1.29 | — |
| vast_ai | $1.3 | — |
| runpod | $1.64 | — |
| gcp | $2.93 | $1.98 |
| aws | $3.06 | $1.96 |
Verdict
At 40 GB, the A100 40GB SXM is a solid mid-to-high-tier choice for translation: single-card up to Qwen 2.5 14B (BF16) or Gemma 2 27B (FP8), and cost-effective at ~$1.19/hr.
See full A100 40GB SXMspecs & pricing, size your model on the calculator, or compare every GPU on the GPU list.