Side by side
| M4 Max | M5 Max | |
|---|---|---|
| Memory sizes | 36, 48, 64, 128 GB | 36, 48, 64, 128 GB |
| Memory bandwidth | 410–546 GB/s | 460–614 GB/s |
| For models, at most | about 96 GB | about 96 GB |
| Models that run well | 47 of 73 | 47 of 73 |
| MLX seat class | Pro | Pro Latest |
| Found in | MacBook Pro (14-inch, M4 Pro or M4 Max, 2024), MacBook Pro (16-inch, 2024), Mac Studio (2025) | MacBook Pro (14-inch, M5 Pro or M5 Max), MacBook Pro (16-inch, M5 Pro or M5 Max), Mac Studio (M5 Max or M5 Ultra) |
Speed with popular models
Tokens a second for one conversation, each chip at its largest memory size (128 GB and 128 GB), with the best version of the model that fits.
| Model | M4 Max | M5 Max |
|---|---|---|
| Llama 3.1 8B Instruct8B | 29–51 8-bit | 33–58 8-bit |
| gpt-oss-20b21B · 3.6B active | 129–230 native 4.25-bit | 145–259 native 4.25-bit |
| Qwen3.6 35B A3B35B · 3B active | 77–137 8-bit | 86–154 8-bit |
| Qwen3.8 27B27B | 11–20 6-bit | 10–17 8-bit |
| Llama 3.3 70B Instruct70B | 3–6 8-bit | 4–7 8-bit |
| gpt-oss-120b117B · 5.1B active | 91–162 native 4.25-bit | 102–182 native 4.25-bit |
| Qwen3.5 122B A10B122B · 10B active | 44–78 4-bit | 49–88 4-bit |
| DeepSeek V4 Flash 0731284B · 13B active | 60–107 2-bit lower quality | 67–120 2-bit lower quality |
Questions
- Is the M5 Max faster than the M4 Max for LLMs?
- The M5 Max reads memory about 12% faster than the M4 Max (614 vs 546 GB/s), so it writes about 12% faster with any model that fits both. Reading a long prompt depends on GPU compute instead, which this comparison doesn’t cover.
- Which can run bigger models, the M4 Max or the M5 Max?
- Neither: both go up to 128 GB. At that size the M4 Max runs 47 and the M5 Max 47 of 73 open models well; the faster one clears more of them at 10 tokens a second.
More comparisons
- M1 Max vs M4 Max
- M2 Max vs M4 Max
- M2 Ultra vs M4 Max
- M3 Max vs M4 Max
- M3 Ultra vs M4 Max
- M4 Pro vs M4 Max
- M4 Max vs M5 Ultra
- M1 Max vs M5 Max
The M4 Max for local AI · The M5 Max for local AI · Compare every model · The best Mac for local LLMs
How it’s worked out
Memory: the model’s parameters at 8, 6, 4, 3 or 2 bits per weight plus MLX’s group scales (half a bit per weight), or its native format, with room for the context on top. By default macOS lets the GPU use about 75% of unified memory. Speed: memory bandwidth divided by the bytes read per token is the ceiling for one conversation; in llama.cpp’s public Apple Silicon benchmarks real runs reach about 45% to 80% of it, which is the range shown. These are estimates, not measurements: measure your own Mac on MLX Bench. Model figures from each official model card, chip figures from Apple’s Tech Specs. Checked 2026-10-19. Same data as mlx.app/compare.