GPU × use-case guide · Vision-language

Is the A100 80GB PCIe a good GPU for vision-language?

The A100 80GB PCIe is a ampere NVIDIA GPU with 80 GB HBM2e (2039 GB/s), 312 TFLOPS BF16 / 312 TFLOPS FP8, and a 300 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 80GB PCIe measures up.

What models fit on a single A100 80GB PCIe?

Weights only, reserving ~25% of the 80 GB for KV cache, activations and fragmentation. ✓ = fits on one card.

Largest single-card fit: Gemma 2 27B at BF16, Mixtral 8x7B (MoE) at FP8, Qwen 2.5 72B at INT4. Bigger models need tensor-parallel across 8 cards.

A100 80GB PCIe for vision-language, specifically

Vision-language is context-heavy, so the KV cache — not the weights — is what fills the 80 GB. On the A100 80GB PCIe 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 80GB PCIe pricing across providers

Verdict

With 80 GB, the A100 80GB PCIe is a data-center-class card that comfortably handles vision-language for models up to Gemma 2 27B at full precision on a single card — a strong pick if your budget supports ~$1.49/hr.