Best Mac for 70B local LLMs
70B models in Q4 need roughly 40GB of weights resident in memory. That rules out anything below 48GB unified memory in practice once you account for the OS and context. The list below is filtered to Macs that meet that floor and ranked by community reports.
Ranked from 9 community reports on llamaperf.
Ranked by community reports
| # | GPU | VRAM | Reports | Fastest t/s |
|---|---|---|---|---|
| 1 | M5 Max 128GBapple | 128GB | 2 | 7.5 |
| 2 | M5 Max 64GBapple | 64GB | 1 | 32.0 |
| 3 | M4 Max 64GBapple | 64GB | 1 | 23.0 |
| 4 | M3 Max 48GBapple | 48GB | 1 | 18.0 |
| 5 | M2 Ultra 64GBapple | 64GB | 1 | 14.0 |
| 6 | M1 Ultra 64GBapple | 64GB | 1 | 12.0 |
| 7 | M3 Max 128GBapple | 128GB | 1 | 5.5 |
| 8 | M2 Ultra 192GBapple | 192GB | 1 | — |
Models that fit
No reports yet
These match the profile but nobody has submitted a report yet.
What to look for
Memory size is the gate
macOS reserves memory for the system and other apps; the practical ceiling for GPU-addressable memory is around 75% of installed RAM by default (configurable via sysctl). On a 64GB Mac, that's ~48GB available — just barely enough for a 70B Q4 with minimal context.
Pro vs Max vs Ultra throughput
M-Pro tier maxes out around 200 GB/s of memory bandwidth, M-Max around 400 GB/s, M-Ultra around 800 GB/s. On a 70B model, expect roughly 3–5 tokens/sec on Pro, 7–12 on Max, and 12–20 on Ultra — directly proportional to bandwidth.
Frequently asked
What's the cheapest Mac that can run a 70B local LLM?
A 64GB Mac (M-Max or M-Pro) is the practical floor for 70B models in Q4 with minimal context. For comfortable use with longer contexts, a 96GB or 128GB Mac is recommended.
How fast does a 70B model run on a Mac Studio Ultra?
Community reports typically show 12–20 tokens-per-second on M2 Ultra and M3 Ultra hardware running 70B models in Q4 quantization, depending on context length and engine (MLX vs llama.cpp Metal).
How we rank
Hardware is sorted by the number of community submissions on llamaperf — a proxy for how widely each card is used in practice for local LLM inference. Within that, we surface the fastest tokens-per-second observed on each as a quality signal. Submissions come primarily from r/LocalLLaMA discussions and direct user uploads. Nothing here is sponsored or affiliate-driven.