Overview
The best local LLM for coding on a Mac is a Qwen3.6 or Qwen3.8 model served by Ollama, because Ollama’s own library page lists the exact ollama launch claude --model qwen3.6 and ollama launch opencode --model qwen3.6 commands for it, and by this site’s arithmetic it fits a 32GB Mac at 65,536 tokens of context. This page does not rank coding quality: it publishes no coding benchmark scores and no tokens per second, and it says unknown where a primary source is silent. It sorts models by what fits each memory tier, using the formulas on methodology, and shows the setup lines from the vendors’ docs.
What is the best local LLM for coding on a Mac?
Pick by memory first, because both coding agents need a long context and the context costs memory.
| Model | Parameters (model card) | Context (model card) | Ollama tag and size | Licence |
|---|---|---|---|---|
| Qwen3.6-35B-A3B | ”35B in total and 3B activated” | 262,144 native, extensible to 1,010,000 | qwen3.6:35b, 23GB - 24GB | apache-2.0 |
| Qwen3.8-27B | 27B | 262,144 native, extensible to 1,000,000 | not on the Ollama page read | apache-2.0 |
| Qwen3.6 27B | 27B | 256K | qwen3.6:27b, 18GB - 19GB | not stated on the Ollama page |
Sizes are copied from the Ollama qwen3.6 page, which also lists MLX builds (qwen3.6:35b-mlx at 24GB, qwen3.6:27b-mlx at 19GB). The Qwen3.6 card says the model “excels in tool calling capabilities” and handles “repository-level reasoning”; those are vendor statements, not tests we ran. Which of the two is better at your code is unknown here.
How much memory does a local coding model need?
Add weights, the KV cache for your context, and the 2 GB overhead assumption from methodology.
The Ollama sizes stand in for weights (upper end of each range). The KV cache uses kv_GB = 2 × layers × kv_heads × head_dim × context × 2 / 1e9. Both cards use a layout of three linear-attention layers to one full-attention layer. This page counts KV for the full-attention layers only, as a labelled assumption. The size of the linear-attention state is unknown, so the totals are lower bounds.
- Qwen3.6-35B-A3B config.json: 40 layers, 10 full-attention, 2 KV heads, head_dim 256.
- Qwen3.8-27B config.json: 64 layers, 16 full-attention, 4 KV heads, head_dim 256.
| Model | Weights used | Context | KV cache | Total with 2 GB overhead |
|---|---|---|---|---|
| Qwen3.6-35B-A3B | 24 GB | 65,536 | 1.342 GB | 27.342 GB |
| Qwen3.6-35B-A3B | 24 GB | 262,144 | 5.369 GB | 31.369 GB |
Qwen3.8-27B (weights from the qwen3.6:27b-mlx tag, 19 GB) | 19 GB | 65,536 | 4.295 GB | 25.295 GB |
| Qwen3.8-27B (same) | 19 GB | 262,144 | 17.180 GB | 38.180 GB |
The 24 GB and 19 GB weights are the MLX tags qwen3.6:35b-mlx and qwen3.6:27b-mlx; no MLX size for Qwen3.8 was read, so treat the Qwen3.8 rows as an estimate. The non-MLX qwen3.8:27b tag is 18 GB on Ollama’s tags page, which gives 24.295 GB at 65,536 tokens and 37.180 GB at 262,144 (the 37.180 GB figure also appears on qwen-on-mac); the 35B GGUF tag is 23 GB. Unquantized, the card parameter counts give weights_GB = params × 16 / 8 / 1e9: 71.9 GB for the 35B (35,951,822,704 BF16 parameters in the safetensors metadata) and 55.6 GB for the 27B (27,781,427,952).
Which Mac memory size do you need for a coding agent?
Apple’s MacBook Pro specs list 16, 24 or 32GB on M5, 24GB base on M5 Pro with 48 or 64GB options (M5 Pro tops out at 64GB), and 36GB base on the 14-inch M5 Max (the 16-inch 40-core GPU model starts at 48GB) with 48, 64 or 128GB higher options. Compared with the totals above, before the unknown macOS reserve:
| Installed memory | Result by the arithmetic |
|---|---|
| 16GB | Neither model fits: the 27b file alone is 18GB - 19GB. |
| 24GB | The 35b file is 23GB - 24GB, and 65,536-token totals are over 25 GB. Not a fit. |
| 32GB | Both fit at 65,536 tokens (27.342 and 25.295 GB). The 35B fits at 262,144 (31.369 GB) with almost no margin. |
| 36GB / 48GB | Both at 65,536 with room. The 27B at 262,144 (38.180 GB) fits 48GB, not 36GB. |
| 64GB | Every row above fits. See best-llm-for-64gb-mac. |
| 128GB | Adds gpt-oss-120b, 69.665 GB at 32,768 tokens by best-llm-for-128gb-mac. |
Whether a Mac Studio with 64GB can run a decent coding model: by this arithmetic it holds both models above, and “decent” is a quality judgement this page does not make. Chip and bandwidth details for the Studio are on mac-studio-llm. Whether it is worth the money is unknown, because prices are not quoted here.
How do you run Claude Code with a local model on a Mac?
Ollama documents two routes. The Ollama Claude Code page gives the launcher and the manual setup:
ollama launch claudeexport ANTHROPIC_AUTH_TOKEN=ollama
export ANTHROPIC_API_KEY=""
export ANTHROPIC_BASE_URL=http://localhost:11434
claude --model qwen3.5The Ollama library page gives the Qwen3.6 form: ollama launch claude --model qwen3.6. The Claude Code page says “For larger repositories, set the context length to 64k or higher.” Ollama needs macOS Sonoma (v14) or newer, and Apple M series gets CPU and GPU support, per the Ollama macOS page. How to set the context length is not on the pages read here, so it is not stated; see ollama-on-mac.
How do you point OpenCode at a local model?
Run ollama launch opencode --model qwen3.6, or configure OpenCode yourself. The Ollama OpenCode page says “OpenCode requires a context length of 64k or higher” and uses the provider @ai-sdk/openai-compatible with baseURL http://localhost:11434/v1 in ~/.config/opencode/opencode.json. Install is curl -fsSL https://opencode.ai/install | bash on macOS. The launcher uses an inline config and does not overwrite your opencode.json. To serve the same model to other machines, see mac-llm-server.
How fast is a local coding model on a Mac?
Unknown. No primary source read gives a speed for these models on a specific Mac, and this site publishes no tokens per second. Measure your own with measure-tokens-per-second-mac. Long contexts and their memory cost are on long-context-on-mac.
Questions
What is the best local LLM for coding on a Mac?
Qwen3.6-35B-A3B or Qwen3.8-27B served through Ollama, because Ollama documents Claude Code and OpenCode launch commands for Qwen3.6 and both cards give a 262,144-token native context. Coding quality against other models is unknown here. Memory totals start at 25.295 GB for the 27B at 65,536 tokens.
Can I use Claude Code with a local LLM on a Mac?
Yes, Ollama documents it: set ANTHROPIC_BASE_URL=http://localhost:11434, ANTHROPIC_AUTH_TOKEN=ollama and an empty ANTHROPIC_API_KEY, then run claude --model with a local model, or use ollama launch claude. Ollama says to set the context length to 64k or higher for larger repositories.
Can OpenCode use a local model on a Mac?
Yes. ollama launch opencode works, or you add an Ollama provider with baseURL http://localhost:11434/v1 to opencode.json. Ollama states OpenCode requires a context length of 64k or higher.
Is a 128GB MacBook Pro a good Claude Code replacement?
It holds more than any 32GB to 64GB Mac, including gpt-oss-120b at 69.665 GB (32,768 tokens). Whether any local model replaces a hosted one is unknown; this page runs no coding tests. See best-llm-for-128gb-mac.
Can a Mac Studio with 64GB run a decent LLM for agentic coding?
By the arithmetic, yes for memory: Qwen3.6-35B-A3B at 262,144 tokens totals 31.369 GB and the 27B 38.180 GB, both under 64GB before the unknown macOS reserve. Quality and speed are unknown.
Is a Mac Studio worth it for local coding agents?
Unknown. Memory fit is covered above, but price and speed are not quoted from a primary source here. Compare configurations on mac-studio-llm and measure speed yourself.
Does ollama launch claude need an API key?
Not for a local model: the manual setup sets ANTHROPIC_AUTH_TOKEN=ollama and leaves ANTHROPIC_API_KEY empty. Ollama Cloud models use a key, which is out of scope here.
How much context does a local coding agent need?
Ollama says 64k or higher for larger repositories on its Claude Code page, states OpenCode “requires a context length of 64k or higher”, and recommends at least 64k tokens for local models on its OpenClaw page. At 65,536 tokens the KV cache for Qwen3.6-35B-A3B is 1.342 GB by the formula above.
Sources read on 2026-10-13
- Ollama: Claude Code: launcher, environment variables, 64k+ context.
- Ollama: OpenCode: launcher, config path, baseURL, 64k requirement.
- Ollama: OpenClaw:
ollama launchbehaviour, “at least 64k tokens” for local models. - Ollama: macOS: macOS 14 or newer, Apple M series CPU and GPU support.
- Ollama library: qwen3.6: tag sizes, 256K window, MLX tags, launch commands.
- Qwen3.6-35B-A3B and its config.json: parameters, context, licence, layers, KV heads, head_dim, safetensors parameter count.
- Qwen3.8-27B and its config.json: same fields.
- Apple MacBook Pro specs: memory options per chip.
- methodology: weight, KV and overhead formulas; best-llm-for-128gb-mac: gpt-oss-120b total.