Best Mac for Local LLMs — Buying Guide
Which Apple Silicon chip should you buy to run local LLMs? This guide is backed by measured benchmark data, not marketing specs.
Why RAM is the primary variable
Unlike GPU inference (where VRAM is the bottleneck), Apple Silicon uses unified memory shared between CPU and GPU. The LLM weights live in this pool. A model that doesn't fit in RAM simply won't run — or will swap to SSD at catastrophically low speeds.
Rule of thumb: multiply the model's parameter count in billions by ~0.5 GB (at Q4 quantization) to get the minimum RAM you need. A 70B model at Q4 needs approximately 35–40 GB. A 32B at Q4 needs ~20 GB.
Benchmark-backed tier recommendations
All tok/s numbers are measured from real hardware runs. Higher is better. Source: benchmarks.json.
Entry: 16–24 GB unified memory $1,300–$2,000
| Chip | Best tok/s | Model | Quant |
|---|---|---|---|
| M4 (10-core GPU, 16 GB) | 76.2 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M4 (10-core GPU, 24 GB) | 75.4 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M4 (10-core GPU, 32 GB) | 75.6 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M3 (10-core GPU, 16 GB) | 67.2 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M3 (10-core GPU, 24 GB) | 64.7 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
Best for 7B models (Llama 3.1 8B, Qwen 3 8B) and 4B MoE models. Comfortable with Q4 quantization at 7B scale. 14B models fit but are tight. 32B models typically require more RAM.
Mid-range: 36–48 GB unified memory $2,000–$3,500
| Chip | Best tok/s | Model | Quant |
|---|---|---|---|
| M4 Pro (20-core GPU, 48 GB) | 118.9 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M4 Pro (16-core GPU, 48 GB) | 111.0 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M3 Max (30-core GPU, 36 GB) | 133.0 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M3 Max (40-core GPU, 48 GB) | 149.0 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M3 Pro (18-core GPU, 36 GB) | 89.8 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M3 Pro (14-core GPU, 36 GB) | 88.2 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
Handles 14B and 30B models comfortably. Q4_K_M 14B runs well, 30B MoE models (Qwen 3 30B A3B ~18 GB) fit with room to spare. 70B models at Q4 need ~40 GB — the 48 GB Pro can run Q4_K_S but headroom is limited.
Professional: 64–96 GB unified memory $3,000–$5,000
| Chip | Best tok/s | Model | Quant |
|---|---|---|---|
| M4 Max (40-core GPU, 64 GB) | 180.3 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M4 Pro (20-core GPU, 64 GB) | 118.6 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M3 Max (40-core GPU, 64 GB) | 107.0 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M3 Max (30-core GPU, 96 GB) | 132.9 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
The sweet spot for serious local inference. Runs 32B dense models (Q4_K_M ~20 GB) with ease, 70B models in Q4–Q5, and leaves headroom for the OS and context cache. M4 Max 64 GB at this tier delivers ~40% higher bandwidth than M3 Max — measurable as faster tok/s on the same model.
High-end: 128 GB+ unified memory $4,500–$10,000+
| Chip | Best tok/s | Model | Quant |
|---|---|---|---|
| M4 Max (40-core GPU, 128 GB) | 182.6 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M4 Max (128 GB) | 184.4 tok/s | Qwen 3 0.6B | Q8_0 |
| M3 Max (40-core GPU, 128 GB) | 146.3 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M3 Ultra (80-core GPU, 256 GB) | 177.9 tok/s | Llama 3.2 1B Instruct | Q4_K - Medium |
| M3 Ultra (60-core GPU, 96 GB) | 34.4 tok/s | Qwen 2.5 14B Instruct | Q4_K - Medium |
Runs 70B dense models at Q8 quality, 235B MoE models (Qwen 3 235B A22B Q4_K_M ~140 GB). For those who want frontier-scale local inference without a server rack. Real cost-benefit threshold: unless you specifically need 70B+ at Q8, 64 GB covers most practical use cases at lower cost.
The rules of thumb
By model size
- 1B–4B models: Any Mac with 8 GB+ runs these fast (100–200 tok/s on M4)
- 7B–8B models: 16 GB minimum; comfortable at 16–24 GB
- 13B–14B models: 16 GB possible (tight); 24 GB comfortable
- 30B–32B models: 32 GB minimum; 48 GB comfortable
- 70B models: 64 GB minimum for Q4; 96 GB for Q8
- 235B MoE models: 128 GB minimum (Q4_K_M ~140 GB)
By use case
- Coding assistant: 7B–14B fast enough for autocomplete; 16–24 GB
- Offline chat: 7B–14B at Q4–Q8; 16–32 GB covers most needs
- Research/reasoning: 32B+ preferred; 48–64 GB
- Multi-agent pipelines: Running multiple models concurrently; 64 GB+
- Frontier local inference: 70B dense or 235B MoE; 96–128 GB+
Chip generation matters too
- M4 Max delivers ~35–40% higher tok/s than M3 Max at same RAM (measured)
- M4 Pro delivers ~50% higher tok/s than M3 Pro (measured, 20-core vs 18-core)
- GPU core count within a generation matters less than RAM ceiling
- If choosing between M3 Max 96 GB and M4 Max 64 GB: pick based on whether you need the 70B+ fit or the speed
What to avoid
- Buying 16 GB if you plan to run 14B+ models — they'll be slow and tight
- Optimizing for GPU core count over RAM — RAM ceiling trumps GPU cores for LLM inference
- Buying a large RAM tier in an older generation when a newer chip with less RAM runs faster
- Over-investing in Ultra chips unless you specifically need 192 GB+ for frontier models
Compare chips side-by-side
Explore by chip
Data
benchmarks.json — full dataset · chips.json — chip summaries · benchmarks.csv
Data sourced from factory lab measurements and community reference runs via LocalScore. See all benchmarks →