## Running local LLMs on a 32GB M5 Pro MacBook Pro Short answer: **a 32GB M5 Pro is a solid, noticeably faster local-LLM machine than your M1 Max 64GB for models that fit — but you lose half the memory ceiling, so the biggest models that fit on the M1 Max won't fit here.** ### The key specs | | M1 Max (your prior machine) | M5 Pro (32GB) | |---|---|---| | Memory bandwidth | 400 GB/s | **307 GB/s** | | Max unified memory | 64 GB | 64 GB (but you're buying 32GB) | | GPU cores | up to 32 | up to 20 | | Neural Engine | 16-core | 16-core + Neural Accelerator in each GPU core | Per Apple's official tech specs, the M5 Pro has a **15- or 18-core CPU, 16- or 20-core GPU, and 307 GB/s memory bandwidth**, configurable to 24/36/48/64GB unified memory ([Apple Support – MacBook Pro M5 Pro/Max Tech Specs](https://support.apple.com/en-us/126318)). Notebookcheck confirms the M5 Pro supports **up to 64GB with a maximum of 307 GB/s** ([Notebookcheck – Apple M5 Pro 18-Core](https://www.notebookcheck.net/Apple-M5-Pro-18-Core-Processor-Benchmarks-and-Specs.1242671.0.html)). ### What this means for speed Memory bandwidth is the #1 driver of token-generation speed on Apple Silicon — token decode streams model weights out of unified memory each step. The rule of thumb is: **tok/s (ceiling) ≈ bandwidth (GB/s) ÷ model size in memory (GB)**, and real-world results land at roughly 50–80% of that ceiling ([LLMCheck – Apple Silicon Memory Bandwidth & LLM Speed](https://llmcheck.net/blog/apple-silicon-memory-bandwidth-llm/)). So on a 307 GB/s M5 Pro: - **8B Q4 (~4–5GB):** ceiling ~60–75 tok/s → realistically **~35–55 tok/s** — very fast, snappy chat/coding. - **14B Q4 (~9GB):** ceiling ~34 tok/s → realistically **~18–27 tok/s** — comfortable. - **27–32B Q4 (~16–20GB):** ceiling ~15–19 tok/s → realistically **~8–14 tok/s** — usable but slower. Note the M5 Pro's 307 GB/s is actually **lower than the M1 Max's 400 GB/s** on paper. However, the M5 generation adds per-GPU-core Neural Accelerators and a much faster Neural Engine, which mainly help **prompt processing (prefill)** — the compute-bound phase — so the M5 Pro should feel more responsive on long prompts even if raw decode bandwidth is a bit lower. Apple claims up to 4x AI performance vs. the previous generation ([Apple Newsroom – M5 Pro/Max MacBook Pro](https://www.apple.com/newsroom/2026/03/apple-introduces-macbook-pro-with-all-new-m5-pro-and-m5-max/)). ### What fits in 32GB With 32GB unified memory, LocalClaw's catalogue fitter says **173 current models fit** under a conservative 8k-context filter, with **Granite 4.2 (8B)** as the recommended starting point. Their top picks for a 32GB M5 include Granite 4.2 8B, Ornith-1.5-9B, Bonsai 2 27B (ternary, ~7GB), Qwen 3 14B, and Gemma 4 12B ([LocalClaw – Best local LLMs for MacBook Pro M5 32GB](https://localclaw.io/hardware/macbook-pro-m5-32gb)). Note this is memory-fit guidance, not measured tok/s. ### The trade-off vs. your M1 Max 64GB - **Wins:** Newer architecture, faster prefill/AI accelerators, better efficiency, and a newer machine overall. - **Loses:** Half the RAM (32 vs 64GB) and slightly lower bandwidth (307 vs 400 GB/s). Models in the **30–70B range** that could load on the 64GB M1 Max (e.g. a 70B Q4 at ~40GB) **will not fit in 32GB** — you'd be limited to roughly ≤27–32B quantized models. ### Bottom line For **7B–14B models**, a 32GB M5 Pro will be excellent — fast, responsive, and great for coding assistants and chat. For **30B+ models**, the 32GB ceiling is the limiting factor, and your old 64GB M1 Max would actually run larger models (albeit with slightly less prefill speed). If large models matter to you, the 48GB or 64GB M5 Pro config is the better buy; if you mostly run 8B–14B, 32GB is plenty. *Caveat: the M5 Pro is a very new chip (announced March 2026), so most published tok/s figures are estimates derived from bandwidth rather than hands-on benchmarks — treat the speed numbers above as informed projections.*