Need low latency model serving for a chatbot

For low-latency chatbot serving, you generally want a setup optimized for fast first-token time and high throughput under concurrent requests. The best choice depends on your budget, model size, and whether you’re serving open-source or proprietary models.

Good serving options

1) vLLM

Best if you’re serving open-source LLMs and want strong latency + throughput.

• Uses paged attention for efficient KV-cache management
• Great for continuous batching
• Works well for chat workloads with many concurrent users
• Common choice for production-style inference

Use when: you need open-source model serving on GPUs and want a strong balance of latency and cost.

---

2) TensorRT-LLM

Best for maximum NVIDIA GPU performance.

• Very low latency
• Highly optimized for NVIDIA hardware
• Can outperform general-purpose servers if carefully tuned

Use when: you’re on NVIDIA GPUs and want the fastest possible serving path.

---

3) Hugging Face TGI (Text Generation Inference)

Solid production server with a simpler operational experience.

• Good batching and streaming
• Easy to deploy
• Supported across common open models

Use when: you want a mature, practical serving stack with decent performance.

---

4) OpenAI / Anthropic / hosted APIs

If you want the lowest operational complexity and don’t need to host yourself.

• Typically very low latency
• No infrastructure management
• Easy scaling

Use when: you care more about product speed than self-hosting.

---

If latency is the top priority, also consider:

• Smaller model: biggest latency win
• Quantization: 8-bit or 4-bit can reduce latency and memory
• Shorter context window
• Streaming responses to improve perceived latency
• Speculative decoding
• Prompt caching
• Batching tuned carefully: helps throughput, but too much batching can hurt tail latency

---

Practical recommendation

For most self-hosted chatbot systems:

• vLLM + a smaller instruct model is the best starting point
• If on NVIDIA and chasing max performance, evaluate TensorRT-LLM
• If you want easiest production deployment, use TGI

---

Rule of thumb

• Single-user / very low latency: smaller model + aggressive optimization
• Many concurrent users: vLLM or TGI with continuous batching
• Maximum GPU efficiency: TensorRT-LLM
• Fastest time to ship: hosted API

If you want, I can suggest a specific stack based on:

1. your model size,
2. GPU type,
3. expected concurrent users,
4. latency target (e.g. <300ms first token).