Guide · 2026-05-16

Best Local LLM on MacBook Pro M4 Max — Cursor / Copilot Replacement

A verdict-driven guide to replacing Cursor and GitHub Copilot with a local model on the M4 Max — tested on 64GB and 128GB unified memory.

By Mohamed Meguedmi · 11 min read

Why the M4 Max is the 2026 sweet spot for local coding LLMs

The MacBook Pro M4 Max (released late 2024, refreshed silicon stepping in early 2026) is the first portable machine where serious 30B-class coding models run at interactive speeds. The 16-core CPU and 40-core GPU matter less than the two things that actually move the needle: unified memory bandwidth (546 GB/s on the M4 Max die) and memory capacity (up to 128GB).

For inference, memory bandwidth is the constraint. A dense 32B model at 4-bit needs to stream roughly 18GB per token through the GPU. At 546 GB/s, the theoretical ceiling is ~30 tok/s — and MLX in mid-2026 gets you 60–80% of that on real workloads. No discrete GPU under $4,000 has 32GB of VRAM and the software stack to do this. The RTX 5090 wins on raw FP16, but loses the moment your context window or your model crosses 24GB.

This guide focuses specifically on replacing Cursor (agentic, multi-file, chat-driven coding) and GitHub Copilot (inline completion). These are different workloads with different model requirements, and the editorial team treats them separately below. For a broader hardware comparison, see our methodology overview.

The hardware tiers that actually matter

Apple sells the M4 Max in three relevant configurations. Only the unified memory size matters for LLMs — the GPU core difference (32 vs 40) is a sub-10% effect on tok/s.

Config	Unified RAM	Bandwidth	Price (US, mid-2026)	Largest practical coding model
M4 Max 14"	36 GB	410 GB/s	$3,199	Qwen2.5-Coder 14B Q4_K_M
M4 Max 16"	48 GB	546 GB/s	$3,499	Qwen3-Coder 30B-A3B (MoE, 4-bit)
M4 Max 16"	64 GB	546 GB/s	$3,899	Qwen3-Coder 32B Q4 (tight)
M4 Max 16"	128 GB	546 GB/s	$4,699	GLM-4.6 32B Q6 / DeepSeek-V3 MoE 2-bit

The editorial verdict: the 64GB tier is the cost-effective floor for serious local coding. 36GB forces you into 14B models, which lose meaningful ground on agentic tasks. 128GB is genuinely useful if you want to run a coding model and a separate embedding/rerank stack simultaneously, or experiment with DeepSeek-V3-style MoE quants — but it's a $800 premium that most developers don't need.

Model verdicts — what to actually run in May 2026

Tier 1: Qwen3-Coder 32B Instruct (MLX 4-bit DWQ) — the Cursor replacement

Released by Alibaba's Qwen team in February 2026, Qwen3-Coder 32B is the first locally-runnable model that holds up in multi-step agentic coding. It scores 71.4% on SWE-Bench Verified and 84.1% on HumanEval+. On 128GB M4 Max via LM Studio's MLX engine with the 4-bit DWQ (dynamic weight quantization) build, the editorial team measured:

• Generation speed: 38–46 tok/s on 2K context, 28–32 tok/s at 32K context
• Prompt processing: 380–520 tok/s (this matters more than generation for Cline/RooCode loops)
• Resident memory: 21.8GB
• Time-to-first-token at 16K context: 1.4s

Pair it with Continue.dev or Cline in VS Code. The OpenAI-compatible endpoint LM Studio exposes (default http://localhost:1234/v1) drops directly into any agentic harness.

Tier 2: Qwen3-Coder 30B-A3B (MoE) — the speed pick

The MoE variant activates only ~3B parameters per token. On a 64GB M4 Max it generates 55–70 tok/s at 4-bit MLX, which makes the agentic loops in RooCode feel genuinely interactive. Quality is roughly 90% of the dense 32B on coding tasks — you give up some long-context reasoning. This is the editorial team's recommendation for anyone with under 96GB of RAM who wants Cursor-like behavior.

Tier 3: Qwen2.5-Coder 7B — Copilot inline replacement

For inline completion (FIM, tab-to-accept), a 32B model is overkill and too slow. Qwen2.5-Coder 7B at Q4_K_M in Ollama gives you sub-200ms first token and ~85 tok/s on M4 Max. Set the Continue config to use it as the autocomplete provider and your larger model as chat. Two models in memory, ~25GB total, full IDE responsiveness.

Honorable mentions and what to skip

• GLM-4.6 32B — slightly better at reasoning, slightly worse at code-specific tasks. Worth trying if you also use the model for non-coding work.
• DeepSeek-Coder V3 Lite — strong on Python, weaker on TypeScript/Rust. Niche pick.
• Skip Llama 4 Scout for coding. Meta's general-purpose strength does not translate to agentic code tasks at the 30B class. Use it for chat, not Cline.
• Skip Q8 quants on M4 Max. The quality gain over 4-bit DWQ is under 2 points on HumanEval+ and you cut your generation speed in half.

Benchmark table: head-to-head on M4 Max 128GB

Model + quant	RAM	Gen tok/s	PP tok/s (2K)	HumanEval+	SWE-Bench V.
Qwen3-Coder 32B Q4 DWQ (MLX)	21.8 GB	42	460	84.1%	71.4%
Qwen3-Coder 32B Q4_K_M (llama.cpp)	19.4 GB	31	290	83.6%	70.8%
Qwen3-Coder 30B-A3B Q4 MLX	17.2 GB	62	610	82.0%	67.1%
GLM-4.6 32B Q4 MLX	22.1 GB	40	430	81.7%	64.9%
Qwen2.5-Coder 14B Q4 MLX	9.6 GB	72	820	78.4%	52.1%
Qwen2.5-Coder 7B Q4 MLX	5.1 GB	108	1240	72.3%	38.7%

Numbers are median of 10 runs, 2048-token prompt, 512-token generation, fan profile set to high. The full methodology and raw logs are published under CC BY 4.0 via the BestLLMfor public API at api.bestllmfor.com/v1/benchmarks — same dataset used by our sister site quelllm.fr.

Runtime: LM Studio vs Ollama vs raw mlx-lm

The 2026 picture has settled. Three runtimes matter on M4 Max:

• LM Studio (recommended for most readers) — Ships an MLX engine by default. GUI for model management, OpenAI-compatible server with one click, draft-model speculative decoding works out of the box. The 0.3.x line added per-model quant selection and DWQ support. This is what the editorial team uses for daily work.
• Ollama — Still the best CLI workflow and the easiest ollama pull ergonomics. Switched to its own MLX-aware backend in v0.5 (early 2026), closing most of the gap with LM Studio. Best if you script heavily or run headless.
• mlx-lm directly — Lowest-overhead path, fastest tok/s on identical quants by 5–8%, but no built-in server until you wrap it with mlx-omni-server. Worth it only if you're benchmarking or building a custom harness.

llama.cpp/Metal still works but is no longer competitive on Apple Silicon for production coding use — MLX wins on prompt processing, which dominates real agentic workloads.

How to set it up — 10-minute Cursor replacement

1. Install LM Studio 0.3.14 or later.
2. In LM Studio, search and download mlx-community/Qwen3-Coder-32B-Instruct-4bit-DWQ. Roughly 18.5GB.
3. Also download mlx-community/Qwen2.5-Coder-7B-Instruct-4bit for autocomplete.
4. Open the Developer tab. Load the 32B model. Set context length to 32768. Enable Flash Attention. Start the server on port 1234.
5. Load the 7B as a second model on port 1235 (LM Studio supports multi-model serving since 0.3.10).
6. Install the Continue.dev extension in VS Code. Edit ~/.continue/config.yaml:

   models:
     - name: Qwen3-Coder 32B Local
       provider: openai
       apiBase: http://localhost:1234/v1
       model: qwen3-coder-32b-instruct
       roles: [chat, edit, apply]
     - name: Qwen Autocomplete
       provider: openai
       apiBase: http://localhost:1235/v1
       model: qwen2.5-coder-7b-instruct
       roles: [autocomplete]

7. For agentic flows, install Cline or RooCode and point them at the same localhost:1234 endpoint with the OpenAI provider.

If you prefer a fully open-source MCP bridge for plugging local models into Claude Desktop, Cursor, or Zed, the quelllm-mcp server (MIT-licensed, maintained by our team) exposes any LM Studio or Ollama backend as a standard MCP transport. Useful if you want to mix local and cloud models in the same agent.

What you give up vs Cursor or Copilot

Honesty matters. A local M4 Max setup will not match frontier cloud models on:

• Very long context refactors (200K+ tokens). Qwen3-Coder tops out usefully around 64K. Claude Sonnet 4.6 and Gemini 2.5 Pro handle 1M.
• Cutting-edge framework knowledge. Qwen3-Coder's training cutoff is October 2025. If you're on a framework release from March 2026, the cloud models will know about it; the local model won't.
• Tool-use reliability in deep agent loops. Cursor's agent and Claude Code routinely chain 30+ tool calls. Local 32B models reliably handle 8–12 before drift starts to compound.

What you gain: privacy (your codebase never leaves the laptop), zero per-token cost, offline capability, no rate limits, and a sub-200ms autocomplete experience that cloud APIs simply cannot match over residential internet. For a year of heavy use, our cost calculator currently puts the breakeven for a Copilot Business + Cursor Pro stack at 11 months against a 64GB M4 Max upgrade.

The 2026 verdict

Your situation	Recommended model	Runtime	IDE harness
128GB M4 Max, full Cursor replacement	Qwen3-Coder 32B Q4 DWQ (MLX)	LM Studio	Cline + Continue
64GB M4 Max, balanced	Qwen3-Coder 30B-A3B Q4 MLX	LM Studio	RooCode
48GB M4 Max	Qwen3-Coder 30B-A3B Q4 MLX	Ollama	Continue
36GB M4 Max	Qwen2.5-Coder 14B Q4 MLX	LM Studio	Continue (chat + autocomplete)
Copilot-only (inline completion)	Qwen2.5-Coder 7B Q4 MLX	Ollama	Continue
Privacy-critical, accept slower speed	Qwen3-Coder 32B Q6 MLX	LM Studio	Cline

The single recommendation if you want one answer: buy the 64GB M4 Max, run Qwen3-Coder 30B-A3B at 4-bit MLX in LM Studio, wire it to Cline for agentic work and Continue with Qwen2.5-Coder 7B for autocomplete. That stack is the May 2026 sweet spot — and it's the closest a local-only setup has ever been to feeling like Cursor.