How do I serve an open-source LLM with low latency for users?

To serve an open-source LLM with low latency, optimize the whole path: model choice, inference engine, hardware, batching, KV cache, and product design. Here’s a practical playbook.

1) Start with the right model

Low latency begins with picking a model that’s small enough for your quality target.

• Prefer 7B–8B models for interactive use if possible.
• Use instruction-tuned variants to reduce prompt engineering overhead.
• Consider smaller/faster models for first response, and a larger model only when needed.
• If quality allows, use quantized models:
  • 8-bit often preserves quality well.
  • 4-bit can significantly reduce memory and improve throughput, sometimes with small quality loss.

2) Use a fast inference engine

Don’t serve with a naive Python loop if you care about latency.

Good options:

• vLLM: great throughput, paged attention, efficient batching.
• TensorRT-LLM: very fast on NVIDIA GPUs, best when you can invest in setup.
• TGI (Text Generation Inference): production-friendly, solid performance.
• llama.cpp: excellent for CPU / edge / small GPU setups.
• SGLang: good for structured generation and efficient serving.

For most GPU-backed chat apps:

• vLLM is a common default.
• TensorRT-LLM if you want to squeeze maximum performance from NVIDIA hardware.

3) Batch requests intelligently

There’s a tradeoff:

• Bigger batches = better throughput
• Smaller batches = lower latency

Use:

• Continuous batching so new requests join an active batch dynamically.
• Micro-batching with a short queue window, e.g. 5–20 ms, to improve throughput without hurting p95 too much.
• Separate policies for:
  • Interactive requests: prioritize low queue time
  • Background jobs: can tolerate more batching

4) Reduce prompt and output length

Token generation cost is linear-ish with tokens, so keep them short.

• Keep system prompts concise.
• Trim conversation history.
• Summarize old turns.
• Limit max output tokens.
• Use retrieval selectively rather than dumping long context.
• Prefer structured prompts that avoid rambling outputs.

A lot of “model latency” is actually too many prompt tokens.

5) Cache aggressively

Caching can dramatically reduce latency.

• Prompt prefix caching: reuse computation for repeated system prompts or shared conversation prefixes.
• KV cache reuse: essential for chat turns.
• Response caching: for repeated or templated queries.
• Embedding/cache for retrieval: avoid recomputing document embeddings.

If many users share the same instructions, prefix caching helps a lot.

6) Optimize the hardware stack

GPU serving

For best latency:

• Use a modern NVIDIA GPU with enough VRAM.
• Prefer GPUs with strong tensor cores and high memory bandwidth.

Common practical choices:

• L4: good cost/perf for moderate workloads
• A10: decent, often economical
• A100/H100: best performance, more expensive

CPU serving

CPU can work for:

• Small models
• Edge deployments
• Very low QPS

But for most chat apps, GPU is easier for low latency at scale.

7) Keep the model hot

Avoid cold starts.

• Keep workers warm.
• Preload weights at startup.
• Avoid autoscaling to zero if latency matters.
• Use health checks and rolling deploys carefully.

8) Stream tokens to users

Users perceive latency by time-to-first-token more than full completion time.

• Stream output as soon as the first token is ready.
• Show typing indicators.
• If possible, generate an initial short answer first, then expand.

This improves perceived latency even if total generation time is unchanged.

9) Use speculative decoding

Speculative decoding can reduce latency significantly.

How it works:

• A small “draft” model proposes tokens.
• The larger model verifies them.
• If accepted, you get faster generation.

This can help a lot for chat-style workloads, especially when the main model is relatively large.

10) Tune decoding settings

Some sampling settings affect speed and user experience.

• Lower max_tokens when possible.
• Use moderate temperature if you want more stable completions.
• Avoid overly complex beam search for chat unless needed.
• Consider greedy decoding for deterministic tasks.

11) Architect for latency end-to-end

Typical production setup:

1. API gateway
2. Request router
3. Inference server with continuous batching
4. Cache layer
5. Observability
6. Autoscaling based on queue time / utilization

Important metrics:

• TTFT = time to first token
• TPOT = time per output token
• p50 / p95 / p99 latency
• GPU utilization
• Queue depth
• Tokens/sec per GPU

12) Control concurrency

Too much concurrency can hurt latency badly.

• Cap maximum simultaneous requests per GPU.
• Use backpressure or admission control.
• Prioritize short requests if you care about responsiveness.
• Separate traffic classes by SLA.

13) Consider model parallelism carefully

Splitting one model across many GPUs can increase latency due to communication overhead.

• For low latency, it’s often better to keep the model on one GPU if it fits.
• Use multi-GPU model parallelism only when necessary.
• If using multi-GPU, ensure fast interconnects (NVLink/fast PCIe).

14) If you need retrieval, optimize that too

For RAG systems, the LLM may be fast but retrieval may dominate latency.

• Use an ANN vector DB with low query latency.
• Cache top-k retrievals.
• Precompute embeddings.
• Keep chunk sizes reasonable.
• Retrieve fewer but better documents.

A good baseline stack

For many production use cases:

• Model: Llama 3 8B Instruct-class model or similar
• Engine: vLLM
• Hardware: L4 / A10 / A100 depending on traffic
• Techniques:
  • continuous batching
  • prompt prefix caching
  • streaming
  • max token limits
  • quantization if acceptable

Rule of thumb

If you want low latency:

• Use a smaller model
• Use a fast serving engine
• Keep prompts short
• Stream output
• Batch carefully
• Cache everything you can

If you want, I can also give you:

1. a reference architecture for low-latency LLM serving,
2. a comparison of vLLM vs TGI vs TensorRT-LLM, or
3. a deployment checklist for one GPU on a cloud VM.