Serving

LLM serving and APIs are the infrastructure and interfaces that deploy AI models as production services — wrapping trained models in scalable API endpoints with authentication, rate limiting, streaming, and monitoring, enabling applications from chatbots to coding assistants to integrate AI capabilities reliably.

What Is LLM Serving?

  • Definition: Deploying trained LLMs as accessible API services.
  • Components: Inference engine, API gateway, load balancing, monitoring.
  • Interface: REST or gRPC endpoints for text generation.
  • Challenge: Scale, latency, reliability, cost efficiency.

Why Serving Infrastructure Matters

  • Production Ready: Models need reliability, not just demos.
  • Scale: Handle thousands of concurrent users.
  • Cost Control: Optimize GPU utilization and expenses.
  • Integration: Clean APIs for application developers.
  • Monitoring: Track performance, usage, and errors.

Serving Architecture

LLM Deployment — From Weights to Production API model artifacts → optimization → containerize → serve → scale → monitor — the MLOps pipeline for LLMs Deployment Pipeline Model weights HF / S3 Quantize AWQ/GPTQ/FP8 Compile TRT-LLM / vLLM Container Docker + GPU Serve REST / gRPC Scale K8s + autoscale Monitor P99/cost Typical timeline: model ready → production API in 1-3 days with vLLM; 1-2 weeks with custom TRT-LLM optimization Infrastructure Options Managed API OpenAI, Anthropic, Google (easiest) Serverless GPU Replicate, Modal, Together (fast start) Dedicated GPU RunPod, Lambda, CoreWeave (control) Cloud VMs AWS p5, GCP a3, Azure ND (scale) On-prem own H100 cluster (data sovereignty) Edge llama.cpp, MLC-LLM, Ollama (local) Production SLA Metrics TTFT time to first token (< 500ms target) TPS tokens per second per user (30-80) Throughput tokens/s/GPU (2000-5000 batch) P99 latency tail latency under load Cost $/1M output tokens (target < $1 for OSS) Uptime 99.9% SLA, graceful degradation Production Optimization Checklist □ Quantize (FP8/INT4) — 2-4× memory savings □ Continuous batching — 5-10× throughput vs naive □ Prefix caching — save 30-50% for shared prompts □ Tensor parallel — split model across GPUs □ CUDA graphs — eliminate kernel launch overhead □ Speculative decoding — 2-3× decode speedup The fastest path to production: vLLM + Docker + Kubernetes + quantized weights = reliable LLM API in hours. LLM deployment is GPU memory management: fit the model, maximize batch size, minimize time-per-token.

Serving Frameworks

Framework     | Strengths                    | Best For
--------------|------------------------------|--------------------
vLLM          | PagedAttention, fastest OSS  | High-volume serving
TGI           | HuggingFace, production      | HF ecosystem
TensorRT-LLM  | NVIDIA optimized, fastest    | NVIDIA hardware
Triton        | Multi-model, enterprise      | Complex pipelines
llama.cpp     | CPU/edge, portable           | Local deployment
Ollama        | Simple local, CLI            | Developer setup

API Design Patterns

Chat Completions API (OpenAI-compatible):

POST /v1/chat/completions
{
    "model": "llama-3.1-70b",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing"}
    ],
    "temperature": 0.7,
    "max_tokens": 1000,
    "stream": true
}

Streaming Response (SSE):

data: {"id":"chatcmpl-123","choices":[{"delta":{"content":"Quantum"}}]}
data: {"id":"chatcmpl-123","choices":[{"delta":{"content":" computing"}}]}
data: {"id":"chatcmpl-123","choices":[{"delta":{"content":" is"}}]}
...
data: [DONE]

Key API Features

  • Streaming: SSE/WebSocket for token-by-token delivery.
  • Function Calling: Structured tool use capabilities.
  • JSON Mode: Guaranteed valid JSON output.
  • Logprobs: Token probabilities for confidence.
  • Stop Sequences: Custom stopping conditions.
  • Seed: Reproducible generation.

Production Considerations

Rate Limiting:

Strategies:
- Requests per minute (RPM)
- Tokens per minute (TPM)
- Per-user quotas
- Per-tier limits

Cost Management:
- Track tokens/cost per user/team.
- Set spend limits and alerts.
- Optimize batch vs. real-time.
- Cache common queries.

Reliability:
- Health checks and auto-restart.
- Graceful degradation.
- Multi-region deployment.
- Automatic failover.

Deployment Options

Managed APIs (Zero infrastructure):
- OpenAI, Anthropic, Google APIs.
- Highest simplicity, lowest control.

Serverless GPU (Minimal ops):
- Replicate, Modal, RunPod, Together.
- Pay per use, automatic scaling.

Self-Hosted Cloud (Full control):
- AWS/GCP/Azure GPU instances.
- Kubernetes with GPU operators.
- Higher ops burden, more control.

On-Premise (Maximum control):
- NVIDIA DGX systems.
- Air-gapped environments.
- Full data sovereignty.

LLM serving and APIs is where AI capabilities meet product requirements — robust serving infrastructure determines whether AI features are reliable and cost-effective or fragile and expensive, making serving engineering essential for any production AI application.

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