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Serving large language models for chat, completion, and agentic workloads. AIMC scores this specific combination 100/100 — excellent fit.
Excellent fit. AIMC's fit score combines VRAM headroom, GPU class match, and FP16 compute against the workload's requirements — independent of pricing.
Listing-weighted median across 4 observed B200 SXM listings at Verda. The same GPU is tracked at 7 marketplaces total.
Top 5 alternative providers for the same GPU, sorted by price ascending.
Alternative high-fit options at the same provider, sorted by fit score.
Serving large language models for chat, completion, and agentic workloads. LLM Inference requires at least 12 GB VRAM and benefits from Datacenter or Workstation or Consumer-class compute.
Full LLM Inference guide and all viable GPUsGet alerts when Verda adjusts pricing on the B200 SXM — useful for sustained llm inference workloads.