# llama-modchip — AI Coding Tool Review & Pricing

> llama-modchip is a set of ggml/llama.cpp kernel patches, quantization transcoders and runtime tools for running mixture-of-experts models entirely in system RAM on CPUs, with an optional GPU for hybrid runs. Its Fractal-64 quantization fits the 284B DeepSeek-V4-Flash into 64 GB of RAM, and KV slot snapshots let coding-agent prompts restore in milliseconds, with a `modchip claude` command that wires Claude Code to the local server.

Source: https://ai.dosa.dev/tools/llama-modchip · Updated Nov 9, 2026 · Category: Hardware & Edge AI · Published by dosa.dev

## At a glance

- **Tool**: llama-modchip
- **Company**: Synchopate
- **Category**: [Hardware & Edge AI](https://ai.dosa.dev/category/hardware-edge)
- **Pricing model**: Free
- **Official website**: https://github.com/synchopate/llama-modchip

## Key features

- Fractal-64 and Fractal-MXFP4 quantization that fits 284B DeepSeek-V4-Flash in 64 GB RAM and 30B MoEs in 16 to 32 GB
- Cache-tiled SIMD kernel patches for AVX-512, AVX2, ARM64 NEON/SVE and Apple Metal
- Self-speculative n-gram drafter and `/dev/shm` KV slot snapshots for fast agent context restore
- `modchip` CLI for setup, chat, web HUD, benchmarks, evals, quantization and `modchip claude` to launch Claude Code against the local 256K-context server
- Hardware readiness check and a community CPU benchmark leaderboard

## Pricing

**Model**: Free

llama-modchip is free and open-source under the Apache-2.0 license with no paid tiers. You supply your own hardware: 16 to 32 GB of RAM for 30B MoE models or 64 GB for the 284B DeepSeek-V4-Flash build.

## Who it is for

- **Best for**: Developers with a 64 GB desktop or workstation and no large GPU who want to run big open MoE models locally as a private backend for Claude Code and other coding agents.
- **Not ideal for**: Users who want stock llama.cpp, Ollama or LM Studio compatibility (Fractal-64 GGUFs need the patched binaries) or a mature, multi-maintainer project, since it launched in late October 2026.

## Verdict

An ambitious CPU-first MoE runtime that makes very large open models usable on ordinary desktops for local agent work, best treated as an experimental fork until its patches settle.

## Recent updates

v2.4.0 shipped on November 4, 2026 after v2.2.0 and v2.3.0 on November 2 and 3, 2026; commits on November 9, 2026 added macOS and Windows hardware detection and automatic Fractal-64 GGUF selection. The project reached the Hacker News front page in early November 2026.

## Frequently asked questions

### Is llama-modchip free?

Yes - llama-modchip is Free. llama-modchip is free and open-source under the Apache-2.0 license with no paid tiers. You supply your own hardware: 16 to 32 GB of RAM for 30B MoE models or 64 GB for the 284B DeepSeek-V4-Flash build.

### Is llama-modchip open source?

Yes - llama-modchip is open source. llama-modchip is free and open-source under the Apache-2.0 license with no paid tiers. You supply your own hardware: 16 to 32 GB of RAM for 30B MoE models or 64 GB for the 284B DeepSeek-V4-Flash build.

### Who is llama-modchip best for?

Developers with a 64 GB desktop or workstation and no large GPU who want to run big open MoE models locally as a private backend for Claude Code and other coding agents.

### Who is llama-modchip not ideal for?

Users who want stock llama.cpp, Ollama or LM Studio compatibility (Fractal-64 GGUFs need the patched binaries) or a mature, multi-maintainer project, since it launched in late October 2026.

### What are the best llama-modchip alternatives?

The closest llama-modchip alternatives on ai.dosa.dev are Copperhead, NVIDIA Personal AI Router (PAIR), WebLLM - all listed under Hardware & Edge AI.

### Who makes llama-modchip?

llama-modchip is developed by Synchopate. It is listed in the Hardware & Edge AI category on ai.dosa.dev.

## Top alternatives

- [Copperhead](https://ai.dosa.dev/tools/copperhead) (Freemium) — Hardware engineers who want to automate design tasks, sync documentation with schematic changes, and maintain consistency in KiCad repositories.
- [NVIDIA Personal AI Router (PAIR)](https://ai.dosa.dev/tools/personal-ai-router) (Open Source) — Multi-agent AI workflows and concurrent inference tasks that benefit from distributing parallel requests across multiple local machines.
- [WebLLM](https://ai.dosa.dev/tools/webllm) (Open Source) — Developers building privacy-first web applications, in-browser AI assistants, or Chrome extensions that require local LLM execution without server-side infrastructure.
- [Magnitude](https://ai.dosa.dev/tools/magnitude) (Free) — Developers and power users running local coding agents on Apple Silicon or NVIDIA hardware who require high-performance, low-latency inference.
- [text-to-cad](https://ai.dosa.dev/tools/text-to-cad) (Open Source) — Rapid prototyping, creating custom brackets or fixtures, robotics development using URDF, and preparing manufacturable files from AI prompts.
- [nanochat](https://ai.dosa.dev/tools/nanochat) (Open Source) — Developers, researchers and students who want to understand or experiment with every stage of LLM training using a readable codebase and a modest GPU budget.
- Full alternatives guide: https://ai.dosa.dev/tools/llama-modchip/alternatives