Run the Mythos Enhanced Coding Model Locally with llama.cpp and Pi

2026-07-22 · Source: KDnuggets · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

The article details how to run the Qwythos-9B-Claude-Mythos-5-1M coding model locally using llama.cpp and integrate it with the Pi coding agent. Released on July 21, 2026, this 9B model, based on Qwen3.5, is optimized for local coding workflows and long-context tasks on consumer hardware. The guide demonstrates installing llama.cpp, configuring a Hugging Face cache, and starting the Q6_K MTP GGUF model with specific parameters like "--ctx-size 100000" and "--spec-type draft-mtp". On an RTX 4070 Ti Super with 16GB VRAM, the setup achieved 81.74 tokens per second. It then covers installing Pi and its "pi-llama" plugin, connecting it to the local llama.cpp server running on port 8910. The Qwythos model, when integrated with Pi, successfully built a browser game and a Python CSV-to-Excel CLI tool, showcasing its capability for practical local development.

Key takeaway

For AI Engineers building local development environments, this guide demonstrates a viable path to run powerful coding agents without external API dependencies. You can deploy Qwythos-9B-Claude-Mythos-5-1M with llama.cpp and Pi to create front-end apps or CLI tools, leveraging MTP speculative decoding for performance. Consider your GPU's VRAM (e.g., 8GB vs. 16GB) when selecting quantization levels to optimize for speed and quality.

Key insights

Qwythos-9B-Claude-Mythos-5-1M enables efficient local coding agent workflows on consumer GPUs via llama.cpp and Pi.

Principles

• Local LLMs reduce API dependency and costs.
• Speculative decoding boosts local inference speed.
• Quantization balances quality and VRAM usage.

Method

Install llama.cpp CLI, set Hugging Face cache, serve Qwythos-9B-Claude-Mythos-5-1M GGUF model with MTP speculative decoding, then install Pi and its "pi-llama" plugin to connect to the local server.

In practice

• Use Q6_K for 16GB VRAM, Q4_K_M for 8GB.
• Reduce "--ctx-size" to manage VRAM/RAM.
• Integrate web search for enhanced agent capabilities.

Topics

• Qwythos-9B-Claude-Mythos-5-1M
• llama.cpp
• Pi Coding Agent
• Local LLM Deployment
• Speculative Decoding
• GGUF Quantization

Code references

• ggml-org/llama.cpp

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

Related on AIssential

• llama.cpp: Fast Local LLM Inference, Hardware Choices & Tuning — llama.cpp is the dominant open-source C/C++ framework enabling fast local large language model (LLM) inference, leveraging hardware advancements like NVIDIA's RTX 5090 and Apple's M4 Ultra for privacy…
• Gemma 4 with Pi Coding Agent & llama.cpp | Build LLM Resource Calculator with NextJS | 🔴 Live — PyCoding Agent with local LLMs can build simple apps but struggles with complex UI overhauls and skill integration.
• Welcome to Open Source AI: Run Your Own Models Locally — Running open-weight models locally offers privacy, cost savings, and performance comparable to cloud APIs through quantization and optimized inference engines.
• DFlash vs MTP: Qwen3.6 Speculative Decoding Benchmarks with vLLM and llama.cpp — Speculative decoding via DFlash or MTP can significantly accelerate LLM inference if configured correctly.
• Best Local LLM for Coding in 2026? — Optimal local coding LLM selection prioritizes hardware fit and practical performance over raw benchmark scores.
• Run Code Llama locally — Code Llama, based on Llama 2, provides open-source code generation with infilling and large context support.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.