GPU × use-case guide · Embeddings

Is the A100 80GB SXM a good GPU for embeddings?

The A100 80GB SXM is a ampere NVIDIA GPU with 80 GB HBM2e (2039 GB/s), 312 TFLOPS BF16 / 312 TFLOPS FP8, and a 400 W TDP. Embeddings workloads care most about maximum batch throughput on small encoder models — pure tokens-per-dollar. Here's how the A100 80GB SXM measures up.

What models fit on a single A100 80GB SXM?

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 SXM for embeddings, specifically

Embeddings is throughput-bound rather than VRAM-bound, so the A100 80GB SXM's 2039 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 80GB SXM pricing across providers

Verdict

With 80 GB, the A100 80GB SXM is a data-center-class card that comfortably handles embeddings for models up to Gemma 2 27B at full precision on a single card — a strong pick if your budget supports ~$1.69/hr.