GPU × use-case guide · RAG chatbot

Is the B200 SXM a good GPU for rag chatbot?

The B200 SXM is a blackwell NVIDIA GPU with 180 GB HBM3e (8000 GB/s), 2250 TFLOPS BF16 / 4500 TFLOPS FP8, and a 1000 W TDP. RAG chatbot workloads care most about interactive latency and steady throughput at moderate concurrency, with enough VRAM for a mid-size model plus a growing KV cache. Here's how the B200 SXM measures up.

What models fit on a single B200 SXM?

Weights only, reserving ~25% of the 180 GB for KV cache, activations and fragmentation. ✓ = fits on one card.

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

B200 SXM for rag chatbot, specifically

RAG chatbot is throughput-bound rather than VRAM-bound, so the B200 SXM's 8000 GB/s of bandwidth and 2250 TFLOPS matter more than raw capacity. Because it's latency-sensitive, run it at low batch sizes on a fast framework (vLLM ≈ 85% MFU) rather than maximising batch. Size it precisely on the calculator.

B200 SXM pricing across providers

Verdict

With 180 GB, the B200 SXM is a data-center-class card that comfortably handles rag chatbot for models up to Mixtral 8x7B (MoE) at full precision on a single card — a strong pick if your budget supports ~$5.99/hr.