low latency inference hosting for LLM API

If you want low-latency hosting for an LLM API, the best option depends on whether you care most about:

• smallest possible latency
• lowest cost
• highest throughput
• easy ops / managed service
• open-source models vs proprietary models

Best options by use case

1) Lowest latency on GPUs you control

Use vLLM or TensorRT-LLM on a dedicated GPU instance

• Good for: self-hosted APIs, high request volume, tight latency budgets
• Why: optimized batching and KV cache handling
• Typical stack:
  • Model server: vLLM or TensorRT-LLM
  • Infra: AWS g5/g6, GCP A2/A3, Azure NC, or Lambda/Runpod/Paperspace
  • Frontend: FastAPI / nginx / Envoy
• Best if: you can manage GPU infra and want predictable performance

2) Fastest time-to-production

Use a managed inference provider Examples:

• Together.ai

• Fireworks.ai

• Modal

• Replicate

• Baseten

• Hugging Face Inference Endpoints

• OpenAI / Anthropic / etc. if proprietary APIs are acceptable

• Good for: minimal ops, scaling, decent latency

• Tradeoff: more expensive than self-hosting at scale

3) Ultra-low latency for smaller models

Use quantized models on CPU or smaller GPUs

• For smaller LLMs (e.g. 1B–8B), you can get strong latency with:
  • 4-bit / 8-bit quantization
  • GGUF + llama.cpp
  • MLX on Apple Silicon
• Good for: edge-ish deployments, lower traffic, cost-sensitive setups
• Tradeoff: lower quality vs larger models

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What matters most for latency

Model-side

• Smaller model = lower latency
• Shorter context = lower latency
• Quantization helps
• Use speculative decoding if supported
• Use continuous batching / dynamic batching
• Avoid huge prompt prefill when possible

Infra-side

• Put the server close to users regionally
• Use persistent warm instances; cold starts hurt a lot
• Prefer GPU instances with enough VRAM
• Use fast networking and local NVMe if caching
• Keep the model loaded in memory at all times

API-side

• Stream tokens immediately
• Keep requests small
• Reuse HTTP connections
• Use timeouts and retries carefully
• Cache frequent prompts/responses if applicable

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Practical recommendations

If you’re building a product now

• Start with vLLM on a dedicated GPU
• Use a 7B–14B instruct model first
• Host in the same region as most users
• Add streaming responses
• Measure:
  • time to first token
  • tokens/sec
  • p50/p95 latency

If you want managed hosting

• Fireworks.ai or Together.ai are often good for open models
• Modal is great if you want a simpler custom deployment
• Baseten is strong for production model serving

If you want open-source + self-hosted + efficient

• vLLM is usually the first thing to try
• If you need maximum throughput and are willing to optimize deeply, look at TensorRT-LLM

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Rule of thumb

• Few requests, need simplicity: managed provider
• Many requests, need control/cost efficiency: self-hosted vLLM
• Need best GPU performance and can tune heavily: TensorRT-LLM
• Small model / edge / cheap: llama.cpp or quantized CPU inference

If you want, I can give you a shortlist of providers by budget, or a reference architecture for a low-latency LLM API.