CortexReach / llm-deploy

What is llm-deploy?

This Python-based llm deploy github repo offers a sanitized example for deploying the Qwen3.6-35B-A3B-FP8 model using vLLM, serving it via an OpenAI-compatible API on `/v1/chat/completions`. It solves the hassle of setting up production-grade llm deployments by providing a Docker Compose config tuned for 4-GPU hardware like RTX 4090s, with support for long contexts up to 131k tokens, multimodal inputs, tool calling, and speculative decoding. Users get a one-command `docker compose up` launch, health checks on port 8000, and built-in benchmarks to measure TTFT and throughput.

Why is it gaining traction?

It stands out as a clean, production-ready llm deployment vllm template without leaked credentials or internal paths, making it ideal for llm deployment best practices and safe experimentation. Developers appreciate the offline mode, FP8 KV cache for efficiency, and scripts for stress-testing concurrency up to 32 parallels—features that deliver real perf insights without custom scripting. Compared to generic llm deployment tools, this nails Qwen-specific parsers for reasoning and tools right out of the box.

Who should use this?

ML engineers evaluating llm deployment in production on consumer GPUs, DevOps teams prototyping vLLM servers for Qwen models, or consultants needing a quick llm deployments baseline before scaling. Perfect for those with 4x 24GB GPUs handling agentic workloads with images/videos and function calls.

Verdict

Solid starter for llm deployment course material or personal rigs, with excellent docs and tests despite 11 stars and 1.0% credibility score. Grab it as a forkable example if you're on matching hardware; otherwise, adapt for broader llm deployment framework needs.

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