best GPU hosting for LLM inference

If you want the best GPU hosting for LLM inference, the answer depends on whether you value price, speed, ease of deployment, scale, or specific GPU availability. Here are the strongest options in practice:

Best overall options

1. RunPod

• Best for: cheapest flexible GPU inference
• Pros: lots of GPU types, simple deployments, good community templates, usually strong price/performance
• Cons: can vary more in reliability/performance than hyperscalers
• Great if: you want to spin up vLLM / TGI / custom inference quickly

2. Lambda Cloud

• Best for: serious training/inference with clean infrastructure
• Pros: strong GPU availability, good performance, generally developer-friendly
• Cons: less “serverless” feel than some platforms
• Great if: you want reliable dedicated GPU instances for production inference

3. Modal

• Best for: easiest serverless deployment for inference
• Pros: very good developer experience, auto-scaling, simple Python-first workflow
• Cons: not always the cheapest for sustained high-volume workloads
• Great if: you want to deploy an LLM API fast with minimal ops

4. Together.ai / Fireworks.ai / Replicate

• Best for: managed LLM inference APIs
• Pros: no infra management, optimized serving, often excellent latency, OpenAI-style APIs in some cases
• Cons: less control, potentially higher cost than raw GPU rental
• Great if: you want to call models via API instead of hosting them yourself

5. AWS / GCP / Azure

• Best for: enterprise, compliance, integration
• Pros: mature security, networking, IAM, scaling, private VPCs
• Cons: usually more expensive and more operational overhead
• Great if: you need enterprise governance or already live in a cloud ecosystem

Best by use case

• Cheapest to get started: RunPod
• Most polished serverless deployment: Modal
• Best dedicated GPU hosting: Lambda Cloud
• Best managed inference API: Fireworks.ai or Together.ai
• Best enterprise choice: AWS/GCP/Azure

GPU types to look for

For LLM inference, prioritize:

• NVIDIA H100 / H200 for top-end throughput
• A100 80GB for strong, often more affordable production inference
• L40S for a good price/performance middle ground
• 4090 for budget/dev workloads, but less ideal for production due to no ECC and consumer-class constraints

Quick recommendation

• If you want to host your own model cheaply: RunPod + vLLM
• If you want clean, reliable dedicated GPUs: Lambda Cloud
• If you want serverless and easy: Modal
• If you want turnkey inference APIs: Together.ai / Fireworks.ai

If you tell me:

1. model size,
2. expected requests/sec,
3. budget,
4. whether you want self-hosted or managed API,

I can recommend the best provider and GPU type more precisely.