Running a 9B coding model at home and hitting 100% on HumanEval - how Agent Zero made it happen
An autonomous AI agent configured a local 9B coding model to match top-tier benchmarks on a 3-year-old RTX 3080.
A developer using the pseudonym 'Agent Zero' has demonstrated that high-performance coding AI can run effectively on consumer-grade hardware. By deploying the OmniCoder-9B model—a Qwen3.5-9B variant fine-tuned on over 425,000 coding agent trajectories—on a system with an AMD Ryzen 9 5900X CPU and an NVIDIA RTX 3080 GPU, they matched the model's official benchmark claims. The key to performance was a precise llama.cpp configuration, including a 128K context window, offloading all layers to the GPU, and crucially, using the `--reasoning-budget 0` flag to disable the model's default chain-of-thought output and force direct code generation.
The most groundbreaking aspect is that the entire optimization and evaluation process was conducted autonomously by an AI agent framework, also called Agent Zero, powered by the GLM-5 model. This 'meta' agent researched configuration details, discovered the need to disable reasoning output, SSH'd into the server to update services, created benchmark scripts from scratch, and ran comprehensive evaluations including HumanEval base, HumanEval Pro, MBPP, and MultiPL-E. This showcases a future where AI systems can self-optimize and evaluate each other, reducing manual engineering overhead. The setup achieved impressive inference speeds of 80-90 tokens per second while using 8.5GB of the available 10GB VRAM, proving that powerful, private coding assistants are accessible without cloud dependencies.
- OmniCoder-9B, a coding-specialized LLM, ran locally on a Ryzen 9/RTX 3080 setup using llama.cpp with a Q6_K quantized 6.85GB model file.
- The `--reasoning-budget 0` flag was critical for disabling chain-of-thought, allowing the model to output code directly and match its claimed 92.7% HumanEval score.
- The configuration and benchmarking was entirely automated by a separate AI agent (Agent Zero using GLM-5), demonstrating autonomous system optimization.
Why It Matters
This proves developers can run state-of-the-art, private coding assistants on affordable hardware, breaking dependency on cloud APIs and their associated costs and privacy concerns.