Mythos 9B Coding Model Runs Locally at 81.74 Tokens Per Second With Pi Integration
Updated
Updated · KDnuggets · Jul 21
Mythos 9B Coding Model Runs Locally at 81.74 Tokens Per Second With Pi Integration
3 articles · Updated · KDnuggets · Jul 21
Summary
Qwythos-9B-Claude-Mythos-5-1M can be served locally through llama.cpp and connected to Pi as a coding agent via an OpenAI-compatible endpoint on port 8910.
A 9B model based on Qwen3.5, it is positioned for consumer hardware; the guide used an RTX 4070 Ti Super with 16GB VRAM running the Q6_K MTP quantization and a 100,000-token context window.
MTP speculative decoding, full GPU layer offload and Flash Attention helped push throughput to about 81.74 tokens per second, while lower-memory users are steered to the Q4_K_M variant or a smaller context size.
In Pi tests, the local setup built a 30-second browser game in a single HTML file and a CSV-to-Excel Python CLI, including tool use, file creation and output verification.
The workflow is pitched as a low-cost local alternative for coding tasks and prototypes, avoiding external API keys and per-request charges while remaining compatible with other OpenAI-style tooling.