LLM Serving Frameworks: How Should I Choose a Runtime?
Choose a runtime that supports your exact model, quantization, and hardware, then benchmark its controls under the intended traffic. vLLM, SGLang, TensorRT-LLM, and llama.cpp are useful candidates for different deployments. No framework name guarantees the lowest latency on your workload.
Check model and hardware compatibility before spending time on load tests.
Pick candidates from the required behavior
| Requirement | Candidate to test | Evidence to collect |
|---|---|---|
| General multi-request GPU serving | vLLM | SLO-compliant throughput and memory pressure |
| Many repeated prefixes or structured generation | SGLang, alongside another supporting runtime | Cache hit rate and output validity |
| NVIDIA-specific optimization | TensorRT-LLM | Model/precision support and latency under load |
| CPU, Apple silicon, or portable GGUF execution | llama.cpp | Fit and speed on the target machine |
| Simple local model setup | Ollama | Whether the local workflow meets the needed concurrency |
These are starting comparisons, not exclusive capabilities. For example, prefix caching and structured output also exist in other serving runtimes.
vLLM documents serving, memory management, scheduling, and deployment options. SGLang includes prefix reuse and structured-generation features. TensorRT-LLM’s quickstart uses a high-level model API and serving command; check the backend and model requirements rather than assuming every deployment needs a prebuilt engine.
Hugging Face archived TGI’s repository in March 2026. Its README recommends alternative engines for new work.
Compare the same request distribution
Use the same model artifact, hardware, precision, prompts, and output caps. Test both unique and repeated prefixes if caching matters. Record time to first token, time per output token, request failures, and throughput at your latency thresholds.
Also test cancellation, malformed input, concurrent long requests, and restart behavior. A runtime that passes a short single-request test may still need different admission limits under load.
The serving-framework comparison in the LLM Engineering Guide connects the runtime choice to inference optimizations.