GPU × use-case guide · Vision-language

Is the A100 40GB SXM a good GPU for vision-language?

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. Vision-language workloads care most about extra VRAM for the vision encoder and image tokens on top of the language model. 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.

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 vision-language, specifically

Vision-language is context-heavy, so the KV cache — not the weights — is what fills the 40 GB. On the A100 40GB SXM you'll trade context length against batch size: long prompts mean fewer concurrent requests. Because it runs offline, batch aggressively to push tokens-per-dollar down. Size it precisely on the calculator.

A100 40GB SXM pricing across providers

Verdict

At 40 GB, the A100 40GB SXM is a solid mid-to-high-tier choice for vision-language: single-card up to Qwen 2.5 14B (BF16) or Gemma 2 27B (FP8), and cost-effective at ~$1.19/hr.