SGLang hosting
SGLang matches or beats vLLM on raw throughput for many workloads and pulls ahead when requests share prefixes (agents, RAG, multi-turn chat) thanks to RadixAttention prefix caching. It also has first-class structured-output (JSON schema) support. DeepSeek and several inference providers run SGLang in production.
Providers with a ready-made SGLang path
| Provider | How | |
|---|---|---|
| RunPod | SGLang template available in template gallery; also common as a Serverless worker base | Deploy |
| Vast.ai | Run via official Docker image lmsysorg/sglang on any instance | Deploy |
| Novita AI | Deploy the lmsysorg/sglang image on a GPU instance | Deploy |
Everywhere else: Official image: `docker run --gpus all lmsysorg/sglang:latest python3 -m sglang.launch_server --model-path <hf-repo>`.
Which GPU to rent
Same VRAM math as vLLM. Budget extra RAM if you enable large prefix caches. That is where SGLang earns its keep.
Live floors refreshed 2026-08-23| Model class | GPU | VRAM | From $/hr | Cheapest at | |
|---|---|---|---|---|---|
| Up to ~17B (FP8) / ~34B (INT4) | RTX 4090 | 24 GB | $0.134marketplace | Vast.ai | Rent |
| Up to ~23B (FP8) / ~46B (INT4) | RTX 5090 | 32 GB | $0.321marketplace | Vast.ai | Rent |
| Up to ~34B (FP8) / ~69B (INT4) | L40S | 48 GB | $0.467marketplace | Vast.ai | Rent |
| Up to ~58B (FP8) / ~116B (INT4) | H100 SXM | 80 GB | $1.34marketplace | Vast.ai | Rent |
| Up to ~102B (FP8) / ~205B (INT4) | H200 | 141 GB | $2.00spot | Verda | |
| Up to ~130B (FP8) / ~261B (INT4) — MoE giants go multi-GPU | B200 | 180 GB | $3.06spot | Verda |
Rent vs API: when self-hosting SGLang pays off
Self-hosting wins when utilization is high and steady. A rented GPU bills every hour whether you use it or not, while APIs bill per token. Run your numbers in the breakeven calculator, or check any open model's page (e.g. Llama 3.3 70B) for a side-by-side estimate.