Qwen 3 (32B)
Near GPT-4 intelligence locally. Thinking mode demolishes hard problems. The local AI dream.
MacBook Pro M4 Max 36GB with 36GB unified memory is a larger local coding and reasoning models machine. This page lists local AI models that fit its memory budget, with realistic performance expectations for LM Studio and similar runtimes.
For MacBook Pro M4 Max 36GB, start with Qwen 3 (32B). Models marked “Comfortable” leave useful memory headroom; “Tight but possible” can work, but you should close other apps and prefer lower quantization.
Near GPT-4 intelligence locally. Thinking mode demolishes hard problems. The local AI dream.
Moonshot AI's agentic flagship. 1T total MoE parameters with 32B active per forward pass. Unmatched long-context reasoning at 256K tokens. Designed for complex agentic tasks and tool use. Model License — check moonshotai.com for commercial terms.
Google's flagship multimodal. Image + text understanding at an exceptional level.
Hybrid reasoning at 32B. Outperforms larger models on reasoning tasks. Strong general purpose.
Qwen 3 VL flagship open vision model. Competes with GPT-4o on MMMU, chart-QA and document reasoning. Native video understanding up to 1 hour. Apache 2.0.
Mistral AI's latest dense 24B. Improved instruction following, function calling, and reduced repetition. Strong European-language support. 128K context. Apache 2.0.
Qwen Team open-weight MoE for agentic coding and multimodal work. 35B total / 3B active, 262K native context, Apache 2.0, and strong GGUF availability through Unsloth and LM Studio-compatible artifacts.
Qwen 3.6 flagship dense model. Hybrid thinking mode with /think toggle for deep chain-of-thought reasoning. 128K context, 29+ languages. Significantly outperforms Qwen3.5-27B on reasoning, coding & math. Apache 2.0.
Qwen flagship coding model. Designed for agentic coding with 256K context. Outperforms Claude 3.5 Sonnet on SWE-bench. Apache 2.0.
PrismML low-bit model derived from Qwen 3.6 27B. Official Apache 2.0 ternary (7.2GB deployed) and 1-bit (3.9GB) builds retain multimodal, reasoning and agentic capabilities through custom GGUF and MLX runtimes.
Gemma 4 MoE flagship-for-workstations: 26B total with ~4B active parameters. 256K context and excellent quality-per-watt for local inference. Apache 2.0.
Largest Gemma 4 model for premium local quality. Strong coding and reasoning with 256K context and broad multilingual support. Apache 2.0.
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