GPU hosting for LLM inference
Choosing LLM hosting starts with the model, its serving software and expected traffic. Memory capacity alone does not establish response speed or throughput.
Editorial guide updated 2026-10-09. Provider facts and prices carry their own dates.Size the complete serving workload
Model weights are only part of memory use. Context length, concurrent requests and serving overhead also matter. Check the inference engine's documentation and test a representative traffic pattern.
Choose your level of control
A pod or VM lets you manage model files and serving software. A managed API handles more operations but exposes its own model catalogue, request limits and billing rules. Compare those trade-offs explicitly.
Evaluate responses and reliability
Measure the latency and throughput your users need. Check rate limits, regional requirements, startup behaviour and failure handling rather than relying on a provider's GPU specifications alone.
Before you choose
- Confirm model licence and serving compatibility.
- Test context length and concurrent requests.
- Check request limits and regional requirements.
Matches use published catalogue evidence. Unknown specifications, unsupported workflows and current inventory must be confirmed with the provider. This guide is not a performance benchmark.
Keep exploring
VRAM requirements · Pods versus serverless · Estimating costs · Compare in ChatGPT