Deploying High-Speed Local Inference with vLLM & Ollama
Configure vLLM with multi-GPU tensor parallelism, enable chunked prefill, set up OpenAI-compatible endpoints, and benchmark production throughput.
- •Deploy vLLM in Docker with NVIDIA Container Toolkit and optimal IPC shared memory
- •Configure iteration-level continuous batching with chunked prefill to eliminate decode stalls
- •Measure TTFT (Time to First Token) and sustained TPS across concurrent request streams
- •Set up Prometheus metrics and Grafana alerts for KV cache eviction and GPU VRAM saturation
Continuous Batching & Iteration-Level Scheduling
Traditional batching locks all requests into the same forward pass; when one request generates 500 tokens and another generates 5, the shorter request waits or wastes computation. vLLM implements iteration-level continuous batching: newly arrived requests join the next decode iteration immediately, and completed requests release their physical KV pages in real time.
vLLM Iteration-Level Continuous Batching Schedule
Prompt enters priority queue while active requests decode token N
Compute prompt KV cache in 512-token chunks without starving decodes
New request joins existing batch in next GPU forward pass
Tokens stream back to HTTP clients via SSE (Server-Sent Events)
Completed request evicts physical KV blocks instantly for recycling
Production Multi-GPU Inference Sizer
vllm serve meta-llama/Llama-3.3-70B-Instruct \ --tensor-parallel-size 2 \ --gpu-memory-utilization 0.90 \ --max-model-len 4096 \ --kv-cache-dtype fp8 \ --port 8000 \ --enforce-eager
Always pass `--enable-chunked-prefill` in vLLM 0.6+. This splits huge prompt prefills into 512-token chunks, ensuring ongoing decode requests never experience latency spikes.
Production vLLM Deployment Suite
Inspect the production infrastructure stack: Docker Compose with IPC shared memory allocation, an asynchronous benchmarking client measuring TTFT, and Prometheus operational alerts.
version: '3.8'
services:
vllm-engine:
image: vllm/vllm-openai:v0.6.3
container_name: vllm_prod_server
runtime: nvidia
restart: unless-stopped
environment:
- HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}
- VLLM_LOGGING_LEVEL=INFO
volumes:
- ~/.cache/huggingface:/root/.cache/huggingface
ipc: host
shm_size: '16gb' # Crucial for NCCL tensor parallelism
ports:
- "8000:8000"
command: >
--model meta-llama/Meta-Llama-3-70B-Instruct
--tensor-parallel-size 4
--gpu-memory-utilization 0.92
--max-model-len 8192
--kv-cache-dtype fp8_e4m3
--enable-chunked-prefill
--port 8000
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 4
capabilities: [gpu]Interactive Algorithm Verification Suite
Click "Run Test Suite" above to execute in-browser verification.
Run this lesson's Triton kernels and multi-file code directly in an ephemeral pre-configured PyTorch 2.5 container.