Inference stacks compared: vLLM, TGI, TensorRT-LLM, llama.cpp, and SGLang

Inference performance is now a core product decision. Most teams choose an inference stack based on latency, cost, and model size, then optimize with caching, batching, and routing.

This guide outlines how the most common stacks fit together and which factors usually determine the choice.

Quick links (for common questions)

If you’re looking for “best inference server for Llama / Qwen”, start at The major stacks and their strengths and A selection checklist.
If you’re trying to hit a p95 latency target, start at What teams optimize for and Benchmarking: what to measure.
If you’re picking a finetuning provider first, read The finetuning platform landscape.
For practical setup guidance, see Docs: Run inference in a container and Docs: REST API.

What teams optimize for

Latency: p50/p95 response time under production load.
Throughput: tokens per second at target quality.
Model coverage: ability to serve the exact model families you ship.
Hardware fit: GPU vendor support, memory constraints, or CPU-only needs.
Operational simplicity: observability, autoscaling, and reliability.

The major stacks and their strengths

vLLM. Popular for fast serving of transformer models with continuous batching and a strong ecosystem. Often chosen when throughput matters and you need rapid iteration. Official project: vLLM.

TGI (Text Generation Inference). Hugging Face's production server focuses on robustness and compatibility with the HF model ecosystem. Good fit if your workflow already uses Hugging Face tooling. Official project: Text Generation Inference.

TensorRT-LLM. NVIDIA's stack focuses on aggressive optimization on NVIDIA hardware. It can deliver excellent latency for production workloads but tends to require deeper systems expertise. Official docs: TensorRT-LLM.

llama.cpp. A lightweight, CPU-friendly option for smaller models or edge-friendly deployments. Frequently used for on-device or cost-sensitive scenarios. Official project: llama.cpp.

SGLang. A newer serving stack that combines programmable control with strong batching and routing features. It is attractive for complex prompts or agentic systems. Official project: SGLang.

Benchmarking: what to measure (so results transfer to production)

End-to-end latency (p50, p95) at realistic concurrency
Token throughput under load (and how it degrades as prompts get longer)
Time to first token (critical for UX)
Memory behavior for long context and multi-turn chat
Stability: tail latency under spikes, retries, and degraded hardware

If you have an eval set, run it through the full request path so you measure quality and performance together (see Docs: Creating evaluations).

A selection checklist

Confirm the stack supports your model weights and quantization strategy.
Benchmark p95 latency at realistic concurrency, not just offline throughput.
Validate operational hooks: tracing, metrics, and error handling.
Test memory behavior for long-context workloads.
Plan how you will roll out model updates and regressions safely.

A layered architecture that keeps options open

A common approach is to standardize on an OpenAI-compatible API surface, then use routing to switch between stacks based on workload. This keeps your product code stable while letting you optimize costs and latency over time.

If you’re using an OpenAI-compatible surface as your contract, see: OpenAI API reference.

FAQ (long-tail queries)

What’s the best inference stack for production?

The “best” is the one that hits your p95 latency target on your hardware while remaining operable by your team. Many teams start with vLLM or TGI for ecosystem and velocity, then adopt deeper optimization (e.g., TensorRT-LLM) when latency or cost becomes the dominant constraint.

Should I use llama.cpp for production?

It can be a great fit when you need CPU-friendly deployments, edge constraints, or smaller models. For high-throughput GPU inference, teams more commonly use GPU-optimized servers.

Bottom line

There is no universal best inference stack. Choose the stack that matches your hardware realities and latency goals, then build a routing layer that keeps you flexible as models and providers evolve.