Where to Buy or Rent GPUs for LLM Inference: The 2026 GPU Procurement Guide (www.bentoml.com)

The 2026 GPU Procurement Guide lays out practical rules for sourcing GPUs to run LLM inference at scale and introduces the Bento Inference Platform as a unifying orchestration layer. It emphasizes that GPU choice is driven first by VRAM (since KV-cache growth with longer context windows often becomes the bottleneck) and second by real-world performance — not marketing specs. The guide recommends benchmarking with tools like llm-optimizer, watching throughput/latency/efficiency, and considering modern inference techniques (speculative decoding, prefill–decode disaggregation, KV-cache offloading). It compares sourcing channels — hyperscalers (AWS, GCP, Azure), specialized GPU clouds (CoreWeave, Lambda), decentralized markets (Vast.ai), and buying hardware/OEMs — and highlights the GPU CAP Theorem: you cannot simultaneously guarantee Control, on-demand Availability, and Price. For practitioners, the big takeaways are operational: NVIDIA remains dominant for production (but AMD’s ROCm and MI300X/MI355X are closing the gap), region-to-region pricing can vary massively (example: H100 costs ~60% more in some regions), and vendor lock-in risks capacity and negotiating leverage. The guide advocates multi-cloud, cross-region or hybrid deployments to handle demand spikes, compliance, and scarcity, and recommends mixing procurement models. Platforms like Bento can automate multi-vendor orchestration and cost-aware routing so teams can focus on model optimization rather than plumbing.