Run DeepCoder on Mac: 2026 Installation Guide (Ollama + MLX)

Last updated April 2026 — refreshed for current model versions, Ollama MLX backend, and macOS 14+ requirements.

DeepCoder is a fully open-source 14B-parameter code reasoning model that rivals OpenAI's o3-mini on standard coding benchmarks — and you can run it entirely on your Mac without sending a single line of code to the cloud. This guide walks through every step from a fresh machine to an interactive coding session, including the 2026 Ollama MLX backend that nearly doubles token generation speed on Apple Silicon.

If you're evaluating whether to run local models at all, or you need vetted developers to integrate AI tooling into your codebase at scale, Codersera's network of remote developers includes engineers who specialize in LLM infrastructure and local AI pipelines.

What changed in 2026 — read this before following any older guide:Minimum macOS is now 14 (Sonoma). Ollama 0.6+ drops support for macOS 10.15–13. The original post listed macOS 10.15 Catalina; that is no longer valid.Ollama now runs on an MLX backend on Apple Silicon (preview, March 2026). Prefill speed jumped from 1,154 to 1,810 tokens/second; decode speed nearly doubled from 58 to 112 tokens/second on M-series chips. Requires 32 GB+ unified memory to enable the preview flag.DeepCoder's Ollama tags are deepcoder:14b (9.0 GB) and deepcoder:1.5b (1.1 GB). The :14b-preview tag referenced in some older guides resolves to the same weights; use deepcoder:14b.Python 3.8 is EOL. Use Python 3.11 or 3.12. The original post listed 3.8 as a requirement; it is no longer supported upstream.ChatBox AI has been superseded by more capable GUI options. Open WebUI and LM Studio are the current recommended frontends (see GUI section below).RAM baseline for the 14B model is now 24 GB minimum, 32 GB recommended. At 16 GB you will be forced to the 1.5B variant.

Want the full picture? Read our continuously-updated Self-Hosting LLMs Complete Guide (2026) — hardware, ollama and vllm, cost-per-token, and when to self-host.

What Is DeepCoder?

DeepCoder-14B-Preview is a code reasoning model released in April 2025 by Agentica and Together AI. It is fine-tuned from DeepSeek-R1-Distilled-Qwen-14B using distributed reinforcement learning — specifically GRPO+ (Group Relative Policy Optimization with clip-high and overlong filtering). Training ran for 2.5 weeks on 32 H100s and used ~24,000 verifiable coding problems sourced from TACO-Verified, PrimeIntellect SYNTHETIC-1, and LiveCodeBench.

The model is licensed under MIT — you can use it commercially, modify it, and redistribute it freely.

What DeepCoder is not

DeepCoder is a code reasoning model with long chain-of-thought output. It is not a chat-optimized general assistant. Prompts with a system message can degrade output quality — the maintainers explicitly recommend putting all context in the user message only.

Benchmark Performance

Source: Hugging Face model card, Agentica; Together AI release blog.

In plain terms: a 14B open-source model hitting o3-mini-level coding performance is exceptional. The practical implication for Mac users is that the 9 GB GGUF file Ollama downloads gives you near-frontier code reasoning without a subscription.

System Requirements (2026)

Hardware

On Intel Macs: The model runs via CPU only. Expect 3–8 tokens/second on a Core i9. Usable, but slow. Apple Silicon uses unified memory as GPU VRAM — the distinction between RAM and VRAM does not apply.

Typical token generation speeds on Apple Silicon (14B Q4_K_M, Ollama 0.20, standard Metal backend):

• M1 Pro 16 GB — model does not fit comfortably; use 1.5B
• M1 Pro / M2 Pro 32 GB — ~18–25 tok/s
• M3 Max 36–48 GB — ~40–55 tok/s
• M4 Pro 24 GB — ~30–40 tok/s (knife-edge; ~7 GB left for context after model loads)
• M4 Max 64 GB — ~60–80 tok/s

Speed is bottlenecked by memory bandwidth, not compute cores. An M3 Max outpaces an M4 Pro on decode speed because it has higher memory bandwidth.

Software

• macOS 14 Sonoma or later (Ollama 0.6+ hard requirement; macOS 15 Sequoia or macOS 26 recommended for MLX)
• Ollama 0.20.6 (latest as of April 2026)
• Python 3.11 or 3.12 (if using the Python API or scripting; Python 3.8–3.10 are EOL)
• Homebrew (optional but recommended for package management)

Step-by-Step Guide: Running DeepCoder on Mac

Step 1: Install Ollama

Ollama is the runtime that manages model downloads, quantization, and the local API server. As of version 0.6+, installation is a single download — no separate binary management required.

Option A — Download the .app (easiest):

1. Go to ollama.com/download/mac and download the macOS package.
2. Open the downloaded .dmg, drag Ollama into Applications.
3. Launch Ollama from Applications or Spotlight. It runs as a menu bar app and starts the API server automatically.

Option B — Homebrew:

brew install ollama
# Then start it:
ollama serve

Verify installation:

ollama --version
# Expected output example: ollama version 0.20.6

When installed as an .app, Ollama starts its API server on http://localhost:11434 automatically at login. You do not need to run ollama serve & manually. If you installed via Homebrew and want background operation, add a launchd service or run ollama serve in a persistent terminal session.

Step 2: Download the DeepCoder Model

Pull the model you want. The 14B file is 9.0 GB — ensure you have a stable connection and at least 16 GB of free disk space (9 GB download + room for Ollama's working files).

14B model (recommended if you have 24 GB+ RAM):

ollama pull deepcoder:14b

1.5B model (for 8–16 GB Macs):

ollama pull deepcoder:1.5b

Monitor the download. Ollama shows progress in the terminal. When complete, verify:

ollama list
# NAME                    ID              SIZE    MODIFIED
# deepcoder:14b           ...             9.0 GB  just now

Step 3: Run DeepCoder in Interactive Mode

ollama run deepcoder:14b

This drops you into an interactive prompt. DeepCoder uses chain-of-thought reasoning — it will emit a <think>...</think> block before its answer. This is normal; do not interrupt it mid-thought.

Example session:

>>> Write a Python function that parses a JWT without a library, verifying the signature using HMAC-SHA256.

<think>
The user wants a pure-Python JWT parser with HMAC-SHA256 verification...
</think>

import hmac, hashlib, base64, json

def verify_jwt(token: str, secret: str) -> dict:
    ...

To exit the interactive session: type /bye or press Ctrl+D.

Critical prompt tip: Do not use system prompts with DeepCoder. Put all context directly in your user message. The model was trained without system prompt conditioning and may produce degraded output when one is present.

Step 4: Interact via the Ollama REST API

Ollama exposes an OpenAI-compatible API at http://localhost:11434. You can call it from any language.

curl (streaming disabled for simpler output):

curl http://localhost:11434/api/generate \
  -d '{
    "model": "deepcoder:14b",
    "prompt": "Write a Node.js Express middleware that rate-limits by IP using a sliding window algorithm.",
    "stream": false,
    "options": {
      "temperature": 0.6,
      "top_p": 0.95,
      "num_predict": 8192
    }
  }'

Python (using the requests library):

import requests

response = requests.post(
    "http://localhost:11434/api/generate",
    json={
        "model": "deepcoder:14b",
        "prompt": "Write a Go function that implements a binary search tree with insert and search.",
        "stream": False,
        "options": {
            "temperature": 0.6,
            "top_p": 0.95,
            "num_predict": 8192,
        },
    }
)
print(response.json()["response"])

OpenAI-compatible endpoint (works with any SDK that accepts a base URL override):

from openai import OpenAI

client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
response = client.chat.completions.create(
    model="deepcoder:14b",
    messages=[{"role": "user", "content": "Refactor this SQL query for performance: SELECT * FROM orders WHERE status = 'pending'"}],
    temperature=0.6,
    max_tokens=8192,
)
print(response.choices[0].message.content)

Recommended inference parameters (from the official model card):

• temperature: 0.6
• top_p: 0.95
• num_predict / max_tokens: at least 8,192 — the model produces long reasoning chains; cutting tokens short mid-thought produces garbage output
• Context window: up to 64K tokens at inference (trained to 32K, generalizes to 64K)

Step 5: Integrate DeepCoder with Your Code Editor

Running the model in a terminal is useful for one-off queries. For daily development, wire it into your editor.

VS Code — Continue extension

1. Install Continue from the VS Code marketplace (1.6M+ installs).
2. Open the Continue sidebar, click the model selector, choose Add model → Ollama → deepcoder:14b.
3. Continue can now: autocomplete inline, answer questions about selected code, and run multi-step agentic edits using DeepCoder as the backend.

Example Continue config snippet (~/.continue/config.json):

{
  "models": [
    {
      "title": "DeepCoder 14B",
      "provider": "ollama",
      "model": "deepcoder:14b",
      "completionOptions": {
        "temperature": 0.6,
        "topP": 0.95,
        "maxTokens": 8192
      }
    }
  ]
}

JetBrains IDEs

The Continue plugin is also available for IntelliJ, PyCharm, GoLand, and other JetBrains IDEs with the same Ollama integration.

Optional: GUI Frontends (2026 Recommendations)

If you prefer a chat interface over the terminal, two frontends are worth using in 2026. ChatBox AI (mentioned in the original post) still works, but the following options have significantly broader adoption and active development.

Open WebUI is the only self-hosted frontend with feature parity to commercial products like Claude.ai or ChatGPT. LM Studio is better for users who want zero configuration on macOS. Both connect to the Ollama backend you configured in Steps 1–2.

MLX Backend: Get 2x Speed on Apple Silicon (Preview, April 2026)

In March 2026, Ollama shipped an MLX-powered backend preview that nearly doubles token generation speed on Apple Silicon. MLX is Apple's machine learning framework built on unified memory, which means the GPU can access the full RAM pool without a separate VRAM copy step.

Benchmarks from the Ollama team (March 29, 2026, Qwen3.5-35B-A3B):

• Prefill: 1,154 tok/s (Ollama 0.18) → 1,810 tok/s (Ollama 0.19 MLX) — 57% faster
• Decode: 58 tok/s → 112 tok/s — 93% faster

Source: Ollama MLX announcement, March 2026.

Requirements for the MLX preview:

• Mac with more than 32 GB unified memory
• Ollama 0.19 or later (current: 0.20.6)
• macOS 14 Sonoma or later

If you have a qualifying Mac, the MLX backend is enabled by setting an environment variable before starting Ollama:

OLLAMA_BACKEND=mlx ollama serve

Note: As of April 2026, the MLX backend is still in preview. Not all models are supported. DeepCoder's GGUF quantization may fall back to the Metal backend automatically on unsupported quantization types — check the Ollama terminal output for confirmation.

How to Choose: DeepCoder vs. Other Local Coding Models

DeepCoder is not the only strong local coding model in 2026. Here is a practical comparison for Mac users:

Decision tree:

• Mac with 8–16 GB RAM: Use deepcoder:1.5b or Llama 4 Scout 8B.
• Mac with 24 GB RAM (M4 Pro base, M3 Pro): deepcoder:14b fits but leaves limited context headroom; Qwen 2.5 Coder 14B is a comparable alternative.
• Mac with 32–48 GB RAM (M3 Max, M4 Max base): deepcoder:14b is the best choice for competitive-quality code reasoning; Qwen 3 Coder 30B-A3B becomes viable.
• Mac with 64 GB+ RAM: Qwen 3 Coder 30B-A3B at Q5/Q6 quantization; DeepCoder 14B fits twice over.
• You want long-context reasoning (>32K tokens): DeepCoder generalizes to 64K context; this is unusual for a 14B model.

Optimizing DeepCoder Performance on Mac

• Free unified memory before running. Close Chrome tabs, Electron apps, and IDEs before starting heavy inference. Every GB of freed RAM is a GB Ollama can use for context.
• Increase Ollama's num_ctx only when needed. Default context in Ollama is 2,048 tokens. For code generation tasks that may produce long outputs or need large input files, increase it: ollama run --num-ctx 32768 deepcoder:14b. More context = more memory consumed.
• Use Activity Monitor → Memory tab to watch memory pressure. Green = fine; yellow = swapping may start soon; red = model is paging to disk and will be unusably slow.
• If experiencing swap: reduce num_ctx, switch to the 1.5B model, or close other apps. Running with memory pressure degrades inference quality because quantization artifacts compound on partial batches.
• MLX backend (if you qualify): Enable with OLLAMA_BACKEND=mlx ollama serve for ~2x decode speed on supported models on 32 GB+ Macs.
• Quantization: Ollama's default GGUF for DeepCoder is Q4_K_M — a good balance of size and quality. Q8 would require ~18 GB and produce marginally better output. Q4_K_M is the practical choice for most users.

Troubleshooting Common Issues

Ollama not found after installation

If you installed via .dmg and get command not found: ollama in Terminal, add the CLI binary to your PATH:

export PATH="$PATH:/Applications/Ollama.app/Contents/Resources"
# Add to ~/.zshrc to persist

Model download fails or stalls

Check disk space first (df -h ~/). Ollama needs ~2x the model size as temporary space during download. If the download stalls, run ollama pull deepcoder:14b again — it resumes from where it left off.

API returns connection refused

The Ollama server is not running. If you installed via .app, open Ollama from Applications. If via Homebrew, run ollama serve in a background terminal. Verify with:

curl http://localhost:11434
# Should return: Ollama is running

Extreme slowness or hang during generation

The model is almost certainly swapping to disk. Check Activity Monitor → Memory Pressure. If it's yellow or red: quit other applications, wait for the pressure to drop, then retry with a shorter num_ctx. If you have only 16 GB RAM, switch to deepcoder:1.5b.

Output cuts off mid-response

The num_predict (max tokens) is too low. DeepCoder emits long think blocks before its answer. Set num_predict to at least 8,192; for complex problems, 16,384 or higher. In Ollama's interactive mode: /set parameter num_predict 16384.

macOS says Ollama is from an unidentified developer

Right-click (or Control-click) the Ollama app in Finder → Open → Open. This bypasses Gatekeeper for locally downloaded apps. You only need to do this once.

Continue extension not finding the model

Ensure Ollama is running before opening VS Code. The Continue extension connects to http://localhost:11434 on startup. If you started VS Code first, reload the window: Cmd+Shift+P → Developer: Reload Window.

FAQ

Is DeepCoder-14B still the best open-source coding model in 2026?

It remains highly competitive for its size. As of April 2026, Qwen 3 Coder 30B-A3B (MoE architecture) is the community top pick for 32 GB+ Macs with its practical MoE efficiency, but DeepCoder 14B holds its own at 60.6% LiveCodeBench and is the better choice for users who prioritize the smallest possible footprint with near-o3-mini quality. The Agentica team has not announced a V2 as of April 2026.

Can I run DeepCoder on a MacBook Air (8 GB or 16 GB)?

On 8 GB: the 14B model will not fit in memory — use deepcoder:1.5b (1.1 GB). On 16 GB: the 14B model is 9 GB but macOS reserves several GB for system processes, so you will likely hit memory pressure and heavy swapping. Stick to the 1.5B model or consider upgrading to 24 GB+ before running 14B models locally.

What macOS version do I need?

macOS 14 Sonoma or later is required as of Ollama 0.6+. If you are on macOS 12 Monterey or 13 Ventura, you can run Ollama 0.5.x (the last version to support those releases) but you will not have access to the MLX backend or newer features. Upgrade macOS if possible.

Does DeepCoder work with GitHub Copilot or Cursor?

Not directly — those tools use their own proprietary backends. For local model integration with an IDE, use the Continue extension (VS Code, JetBrains) or configure LM Studio as a local OpenAI proxy if your editor supports a custom API endpoint.

How do I update the model when a new version is released?

ollama pull deepcoder:14b
# Ollama downloads only changed layers, similar to Docker layer caching

Can I use DeepCoder offline?

Yes. Once downloaded, DeepCoder runs entirely offline. No network calls are made during inference. This is one of the primary reasons to run local models.

My Mac has a dedicated GPU (eGPU). Will Ollama use it?

Ollama uses Metal for GPU acceleration on macOS. Metal supports Apple Silicon's integrated GPU and AMD discrete GPUs. NVIDIA eGPUs do not have Metal support on macOS — they are unusable for Ollama acceleration. The model will fall back to CPU on an NVIDIA eGPU.

How does DeepCoder compare to using the Claude API or DeepSeek API for coding?

Cloud APIs (Claude Sonnet 4.6, DeepSeek V3) produce higher quality output than any local 14B model on complex real-world codebases. The tradeoff is privacy, latency, and cost. DeepCoder is the right choice when: (a) your code is confidential and cannot leave the machine, (b) you need offline access, or (c) you are running high-volume automated code generation where cloud API costs are prohibitive. For most individual developers, a hybrid setup — local DeepCoder for autocomplete, cloud API for complex tasks — is practical. If your team needs AI-integrated development at scale, hiring developers with AI toolchain experience is worth considering.

References and Further Reading

1. DeepCoder-14B-Preview Model Card — Hugging Face (Agentica)
2. DeepCoder: A Fully Open-Source 14B Coder at O3-mini Level — Together AI Blog
3. DeepCoder on Ollama Model Library
4. Ollama is now powered by MLX on Apple Silicon — Ollama Blog (March 2026)
5. Ollama macOS Download Page (current version)
6. Continue — AI Code Agent for VS Code (VS Code Marketplace)
7. Ollama macOS Documentation
8. Best Local LLMs for Mac in 2026 — InsiderLLM (M1–M4 tested)

Related Codersera guides: Run DeepSeek Janus-Pro 7B on Mac · Running OlympicCoder-7B on macOS