# How much RAM does a local LLM need on a Mac? Measurements on 16 GB and a 16–128 GB sizing table > Source: > Published: 2026-10-28 08:28:00+00:00 "Will this model run on my Mac?" usually gets answered with the model's file size. That's only a third of the answer. While a model runs, memory goes to three things: the weights, the context (KV cache) and macOS with everything else you have open. I measured three models on my MacBook M5 with 16 GB, calculated the context cost for classic 8B–70B models, and put together what actually fits into 16 to 128 GB. `recommendedMaxWorkingSetSize` is 12.7 GB, about three quarters. The rest belongs to macOS, the browser and the editor. So the working rule is: **weights + context ≤ ¾ of your Mac's RAM.** You can raise the GPU limit with `sysctl iogpu.wired_limit_mb`, but then apps get less and macOS starts swapping. Setup: Apple M5, 16 GB, Ollama 0.31, MLX builds. Each model ran in three modes: a short request (a 150-word business email) with a 4K window; a 4.2K-token document (~10 pages) with an 8K window; an 18K-token document (~45 pages) with a 32K window, asking for five key takeaways. Memory is what `ollama ps` reports. Answers were capped at 200 tokens. | Model, mode | Memory | Prompt reading | Generation | Total time | |---|---|---|---|---| | Qwen 3.5 0.8B, short | 1.08 GB | — | 109 tok/s | 3 s | | Qwen 3.5 0.8B, 10 pages | 1.14 GB | 2257 tok/s | 103 tok/s | 4 s | | Qwen 3.5 0.8B, 45 pages | 1.73 GB | 1952 tok/s | 86 tok/s | 12 s | | Qwen 3.5 4B, short | 3.43 GB | — | 34 tok/s | 8 s | | Qwen 3.5 4B, 10 pages | 3.59 GB | 390 tok/s | 32 tok/s | 18 s | | Qwen 3.5 4B, 45 pages | 4.81 GB | 312 tok/s | 27 tok/s | 67 s | | Gemma 4 12B, short | 7.64 GB | — | 25 tok/s | 14 s | | Gemma 4 12B, 10 pages | 8.29 GB | 102 tok/s | 16 tok/s | 57 s | | Gemma 4 12B, 45 pages | 9.21 GB | 72 tok/s | 9 tok/s | 4.8 min | What stands out: In classic architectures (Llama 3, Qwen 3 and most 2025 models) every layer caches the whole window. The size follows from the config: 2 × layers × KV heads × head dim × 2 bytes. From the `config.json` files on Hugging Face, at 16-bit (the default in Ollama and llama.cpp): | Model | Q4 weights | Cache per token | 8K | 32K | 128K | |---|---|---|---|---|---| | Llama 3.1 8B (32 layers) | ~5 GB | 128 KB | 1 GB | 4 GB | 16 GB | | Qwen3 14B (40 layers) | ~9 GB | 160 KB | 1.25 GB | 5 GB | 20 GB | | Qwen3 32B (64 layers) | ~19 GB | 256 KB | 2 GB | 8 GB | 32 GB | | Llama 3.3 70B (80 layers) | ~40 GB | 320 KB | 2.5 GB | 10 GB | 40 GB | | Qwen 3.5 4B / Gemma 4 12B (my measurement) | 3.4–7.6 GB | ~50 KB | 0.4 GB | 1.5 GB | ~6 GB | At a full 128K window, a classic model's cache weighs as much as the 4-bit model itself. A 70B model with that window needs 80 GB. Applying "weights + context ≤ ¾ RAM" with the classic-model cache numbers above (newer models with a lean cache fit with room to spare). This is a calculation, not a measurement on every machine: | Mac RAM | For the model | Short requests, 8K | Documents, 32K | Whole books, 128K | |---|---|---|---|---| | 16 GB | ~12 GB | up to 14B, tight | 8B; Gemma 4 12B (9.2 GB measured) | only new-gen 4B | | 24 GB | ~18 GB | 14B comfortably | 14B (14 GB) | only new-gen up to 12B | | 32 GB | ~24 GB | 32B, tight (21 GB) | 14B comfortably | 8B (21 GB) | | 48 GB | ~36 GB | 32B comfortably | 32B (27 GB) | 14B (29 GB) | | 64 GB | ~48 GB | 70B, tight (43 GB) | 32B comfortably | 32B only with a compressed cache | | 96 GB | ~72 GB | 70B comfortably | 70B (50 GB) | 32B (51 GB) | | 128 GB | ~96 GB | 70B comfortably | 70B comfortably | 70B (80 GB) | `num_ctx`, in LM Studio it's Context Length.` OLLAMA_KV_CACHE_TYPE=q8_0` (with `OLLAMA_FLASH_ATTENTION=1`) stores the cache in 8 bits and halves it, with little quality impact. That lets a 32B model with a 128K window fit in 64 GB. How much memory does your Mac have, and which local model do you actually run every day? *Originally published at [klukyanov.ru](https://klukyanov.ru/notes/llm-memory-mac/).* *Shorter weekly write-ups (in Russian) — on [Telegram](https://t.me/+KSAy8mfFOsgwYWMy).*