Run Claude Code 100% on-device with local AI on Apple Silicon.
No cloud, no API key, no proxy — an MLX-native server that speaks the Anthropic API.
🥊 Pick your fighter: Gemma 4 31B · Llama 3.3 70B · Qwen 3.5 122B · DeepSeek V4 Flash (1M context via ds4).

🛑 Usage Limit · 🤔 What Is This · 🚀 Quick Start · 🥊 Lineup · 🎮 Modes · 🔒 Privacy · 📊 Benchmarks · 🎤 Voice · 🌐 Browser · 📱 Phone · 🔌 MCP · 🛣️ Roadmap

If Claude Code just told you "you've reached your usage limit" and gave you a reset time hours away, that's what this is for. You keep working — same Claude Code, same terminal, same project — except the model answering is running on your own Mac.

curl -fsSL https://raw.githubusercontent.com/nicedreamzapp/claude-code-local/main/install.sh | bash

No API key. No second subscription. No waiting until 3pm. It works on a 16 GB MacBook and gets better the more RAM you have — see what runs on your Mac.

Real session, unedited. Claude Code reads and edits the file — the model answering is Gemma 4 31B on the laptop.

Your Mac has a powerful GPU built right into the chip. This project uses that GPU to run massive AI models — the same kind that power ChatGPT and Claude — entirely on your computer, and plugs them into Claude Code so the whole coding experience works offline.

🚫 No internet needed 💰 No monthly subscription 🔒 No one sees your code or data ✅ Full Claude Code experience — write code, edit files, manage projects, control your browser, or run a full hands-free voice session

         📱 You (Mac or Phone)
          │
     🤖 Claude Code           ← the AI coding tool you know
          │  HTTP localhost:4000
     ⚡ MLX Native Server      ← this repo (~1000 lines of Python)
          │
     🥊 Pick your fighter     ← Gemma 4 31B · Llama 3.3 70B · Qwen 3.5 122B
          │
     🖥️  Apple Silicon GPU    ← your M-series chip does all the work

The trick: Claude Code speaks the Anthropic API. Local model servers speak the OpenAI API. So everyone bolts a translation proxy in between — and the proxy is slow and fragile. This server speaks Anthropic natively. One process, zero translations:

🎯 That one change — eliminating the proxy — made it 7.5× faster.

A real NDA. Llama 3.3 70B. Wi-Fi physically OFF. lsof running live. Watch a 70-billion-parameter model audit a confidential legal document, on-device, with the receipts on screen.

AirGap is this whole build running as one private workstation — a capability, not a product. Everything you need is in this repo. If your firm needs one built, here's what it looks like.

More local-AI demos on the channel:

We started with one model. Now we ship a roster. Same MLX server, same Anthropic API — swap one env var and you swap the brain. Plus the ds4 engine for DeepSeek V4 Flash via its own native Metal runtime.

💻 Got a 16 GB MacBook Air? Start with Hermes. setup.sh picks it for you automatically — you don't need 96 GB of RAM to use this.

💡 Fun fact: Qwen wins raw speed because it's an MoE — only 10B of 122B params activate per token. DeepSeek V4 Flash is even bigger (284B) but only ~37B active per token, and it ships with on-disk KV cache so a 25k-token Claude Code system prompt prefills exactly once, ever.

We tested it the day Antirez (the Redis guy) shipped ds4. Local DeepSeek beat cloud Claude on wall-clock time on the same MacBook, same prompt — watch the three-way.

The models in this lineup aren't from generic mirrors — we package and upload our own abliterated MLX builds to HuggingFace so anyone running this repo can pull them with one command. Browse the full set at huggingface.co/divinetribe.

# Llama 3.3 70B — full-precision feel
MLX_MODEL=divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx \
  bash scripts/start-mlx-server.sh

# Gemma 4 31B — fast daily driver
MLX_MODEL=divinetribe/gemma-4-31b-it-abliterated-4bit-mlx \
  bash scripts/start-mlx-server.sh

# Hermes 4 14B — sweet spot for 16/32 GB Macs
MLX_MODEL=divinetribe/Hermes-4-14B-abliterated-4bit-mlx \
  bash scripts/start-mlx-server.sh

Abliteration sources: huihui-ai (Llama, Gemma) and Babsie (Hermes). MLX conversion + quantization by us. See what abliteration means.

⚠️ Use it responsibly. "Abliterated" suppresses the model's built-in refusal direction so it doesn't refuse benign-but-edgy requests. It is not a general capability upgrade, and you remain bound by each upstream license (Llama 3.3, Gemma, Hermes/Qwen3).

Four ways to run the lineup. Each one is a double-clickable launcher in launchers/.

Also need:

• 🐍 Python 3.12+ (for MLX)
• 🤖 Claude Code (npm install -g @anthropic-ai/claude-code)

curl -fsSL https://raw.githubusercontent.com/nicedreamzapp/claude-code-local/main/install.sh | bash

Or clone it yourself if you'd rather read the script first:

git clone https://github.com/nicedreamzapp/claude-code-local
cd claude-code-local
bash setup.sh

setup.sh auto-detects your RAM, picks a model from the lineup, downloads it, installs the MLX server, and creates a Claude Local.command launcher on your Desktop.

Then double-click Claude Local.command. You're coding locally.

🐛 If the launcher asks you to sign in to a Claude account: your claude CLI is too old. The launchers pass --bare to force local-only API-key auth; older CLIs don't support it. Fix: npm install -g @anthropic-ai/claude-code

🛠️ Note for contributors: setup.sh installs the server as a symlink at ~/.local/mlx-native-server/server.py pointing back at this repo's proxy/server.py. Edit the file in the repo, restart the server, done — one source of truth, no silent drift.

# 1. Set up the MLX virtualenv
python3.12 -m venv ~/.local/mlx-server
~/.local/mlx-server/bin/pip install mlx-lm

# 2. Pick a fighter and download (one time, ~18-75 GB)
bash scripts/download-and-import.sh gemma   # or 'llama' or 'qwen'

# 3. Start the server
MLX_MODEL=divinetribe/gemma-4-31b-it-abliterated-4bit-mlx \
  bash scripts/start-mlx-server.sh

# 4. Launch Claude Code
ANTHROPIC_BASE_URL=http://localhost:4000 \
ANTHROPIC_API_KEY=sk-local \
claude --model claude-sonnet-4-6

┌──────────────────────────────────────────────────┐
│              Your MacBook (M-series)             │
│                                                  │
│  📝 You type ──> 🤖 Claude Code                  │
│                      │                           │
│                      ▼                           │
│                 ⚡ MLX Server (port 4000)        │
│                      │                           │
│                      ▼                           │
│                 🥊 Local model ──> 🖥️  GPU        │
│                 (Gemma·Llama·Qwen)               │
│                      │                           │
│                      ▼                           │
│  📝 Answer <─── ✨ Clean response                │
│                                                  │
│         🔒 Nothing leaves this box. Ever.        │
└──────────────────────────────────────────────────┘

The server (proxy/server.py) is one file, ~1000 lines. It does six things:

1. 📦 Loads the model — Apple's MLX framework, native Metal GPU, unified memory. Handles Gemma's RotatingKVCache quirk automatically.
2. 🔌 Speaks Anthropic API — Claude Code thinks it's talking to Anthropic's cloud. It's not.
3. 🔧 Translates tool use — Three tool-call formats in and out: Gemma 4 native, Llama 3.3 raw JSON, and HuggingFace <tool_call> JSON (Qwen and others). All converted ↔ Anthropic tool_use blocks, with garbled-output recovery for small models.
4. 🧹 Cleans the output — A real-time ThinkingFilter strips <think> blocks token-by-token during generation, then clean_response handles stop markers and reasoning preamble.
5. ⚡ Reuses prompt caches across requests — Claude Code's system prompt doesn't get re-prefilled every turn. Huge speedup for short questions.
6. 🎯 Code mode — auto-detects Claude Code coding sessions, swaps the ~10K-token harness prompt for a slim ~150-token one, and strips verbose tool descriptions to name + parameter types. A 28× prompt reduction that cuts prefill from ~60 s to ~2 s on Gemma 4 31B.

We didn't start here. Three generations in one night:

This is the part we're proudest of. Your code never leaves your Mac. Not for a model call. Not for telemetry. Not for "anonymous analytics". Not ever.

   ┌─────────────────────────────────────────────────────────────┐
   │                    🖥️  YOUR MACBOOK                          │
   │                                                             │
   │   📝 Your code ──> 🤖 Claude Code ──> ⚡ MLX Server          │
   │                     (localhost:4000)      │                 │
   │                                           ▼                 │
   │                    🧠 Local model ──> 🖥️  Apple GPU          │
   │                                                             │
   │             🚫 ZERO outbound network calls                  │
   │             🚫 ZERO telemetry                               │
   │             🚫 ZERO phone-home                              │
   └─────────────────────────────────────────────────────────────┘
                   │
                   ✗  ←  Nothing from our code crosses this line.
                   │
   ┌─────────────────────────────────────────────────────────────┐
   │                    ☁️  THE INTERNET                          │
   │                  (your code never goes here)                 │
   └─────────────────────────────────────────────────────────────┘

✅ Verified offline. Claude Code's own binary previously reached out to api.anthropic.com on startup for telemetry, statsig feature flags, marketplace auto-install, and the autoupdater. The launchers plug all four channels via documented Anthropic env vars (thanks @tadrianonet, PR #32):

CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1
DISABLE_AUTOUPDATER=1
CLAUDE_CODE_DISABLE_OFFICIAL_MARKETPLACE_AUTOINSTALL=1
CLAUDE_CODE_DISABLE_BACKGROUND_TASKS=1

Verify it yourself: run lsof -p $(pgrep -f claude) during a session — you'll see only localhost:4000. Run lsof -i -P while the server is up — nothing leaves your Mac.

⚠️ We removed LiteLLM after supply-chain attack concerns. Every dependency was re-audited from scratch. If a package had unexplained network calls, it didn't ship.

Three generations of optimization. Each one got faster.

💡 Our local setup beats cloud Opus on raw speed (65 vs 40 tok/s) at $0/month. Qwen numbers measured on M5 Max 128 GB — full details in BENCHMARKS.md.

Local models don't format tool calls perfectly. They want to call a tool but mix XML and JSON syntax — Claude Code sees no valid tool call, re-prompts, and the model garbles it the same way again. The result: infinite loops where the AI says "let me do that" but never does anything.

We fixed this with 4 changes to server.py:

🧪 Results: 98/98 tests passed across 7 consecutive runs. Zero failures. The multi-step scenario that used to trigger infinite loops — create 12 month folders, delete all but September, verify — now passes every time. Run it yourself:

python3 scripts/test_mlx_server.py

Everything above gets you running. These live in docs/ so this page stays short:

claude-code-local is the brain. It pairs with sibling repos — each stands alone, together they take Claude Code off the keyboard and off the screen:

We ship fast and in public. If any of these excite you, hit Watch to get the release ping.

• 🟡 Full Qwen 3.5 122B benchmark suite — reliability, tool-call pass rate, long-context behavior vs Gemma
• 🟡 Fully-local Whisper fallback — alternative to the Apple SFSpeechRecognizer path for older Macs and non-English voices
• 🟡 One-click DMG installer — no terminal needed
• 🟡 MLX_MODEL=<hf-url> — point at any HuggingFace repo and auto-register a new fighter
• 🟡 More fighters — open to PRs adding launchers for DeepSeek, Mistral, Phi, anything MLX-compatible

💡 Want something that's not on this list? Open an issue → Every serious request gets read and usually replied to within 24h.

Ideas, bug reports, a new launcher for a model I don't run, a better code-mode prompt — open an issue or a PR, I read them all. Especially interested in: folks on older Apple Silicon (M1/M2, 16–36 GB) who know which models actually fit; anyone stress-testing the voice loop on different hardware or accents; TTS recipes beyond Pocket TTS (Piper, MLX-TTS, Kyutai Moshi); and edge cases I'll never hit on an M5 Max with 128 GB.

Every one of these landed on hardware I don't own, on a bug I hadn't hit. Thank you.

Tested on Apple M5 Max with 128 GB unified memory.

Built by Matt Macosko in Arcata, CA — part of Nice Dreamz LLC. More open-source at nicedreamzwholesale.com/software · demos at youtube.com/@nicedreamzapps.

📜 MIT License — Use it however you want.

💬 Builders hang out on Discord — share what you're building, swap MLX tips.

⭐ Star this repo if it helped you! ⭐

Upstream projects this is built on are listed in docs/CREDITS.md.