M4 Max vs M4 Pro
Same generation. Different tier. Is M4 Max worth the premium for local AI inference?
Both chips share the same M4 architecture. The question is whether the Max tier's extra GPU cores and higher memory bandwidth translate to meaningfully faster LLM inference — and whether the RAM ceiling difference matters for the models you want to run.
Benchmark comparison — 3 shared models
Best published result for each model on each chip family. Q4_K Medium quantization throughout. Higher tok/s is better.
| Model | M4 Pro (best) | M4 Max (best) | Difference |
|---|---|---|---|
| Llama 3.2 1B Instruct Q4_K - Medium |
119.2 tok/s M4 Pro 20-core GPU, 24 GB |
182.6 tok/s M4 Max 40-core GPU, 128 GB |
+53% |
| Llama 3.1 8B Instruct Q4_K - Medium |
32.9 tok/s M4 Pro 20-core GPU, 64 GB |
55.1 tok/s M4 Max 40-core GPU, 48 GB |
+67% |
| Qwen 2.5 14B Instruct Q4_K - Medium |
18.0 tok/s M4 Pro 20-core GPU, 64 GB |
30.1 tok/s M4 Max 40-core GPU, 48 GB |
+67% |
Data source: benchmarks.json. Reference run data from LocalScore community aggregation.
Chip specs compared
| Spec | M4 Pro (20-core GPU) | M4 Max (40-core GPU) |
|---|---|---|
| GPU cores | 16 or 20 | 32 or 40 |
| Memory bandwidth | ~273 GB/s | ~546 GB/s |
| Max unified RAM | 64 GB | 128 GB |
| Neural Engine | 38 TOPS | 38 TOPS |
| LLM inference sweet spot | 7B–14B models | 7B–70B models |
| Can run 70B models | No (64 GB ceiling) | Yes (at Q4 with 128 GB) |
Memory bandwidth is the primary driver of LLM inference speed on Apple Silicon. M4 Max has roughly 2× the bandwidth of M4 Pro, which explains the ~65% throughput advantage on 8B models (the gap doesn't scale 2× because other bottlenecks exist).
Who should choose which
Choose M4 Pro if…
- You primarily run 7B–14B models
- Budget is constrained ($2,000–$3,500 range)
- You need a laptop (MacBook Pro M4 Pro)
- Speed of 30–35 tok/s on 8B is fast enough
- You are not planning to run 30B+ models
Choose M4 Max if…
- You want to run 32B+ models at usable speed
- You want the highest throughput for real-time UX
- You are running a coding assistant all day (latency matters)
- You plan to run 70B models (requires 128 GB config)
- You are building products that depend on local inference
Verdict
The 67% advantage on Llama 3.1 8B and Qwen 2.5 14B is not a small rounding difference. At 32–33 tok/s on M4 Pro vs 55 tok/s on M4 Max, M4 Max output feels noticeably faster during sustained use. For a coding assistant or chat session, the difference is real. The bigger unlock is RAM: M4 Pro tops out at 64 GB, limiting you to ~32B models at Q4. M4 Max can be configured to 128 GB, enabling 70B inference.
Bottom line: if you are serious about local AI and can afford M4 Max, the throughput and RAM ceiling make it the right choice. M4 Pro is excellent for developers who primarily run 7B–14B models and want the best price-per-performance in that range.
Chip pages
Related comparisons
Data
benchmarks.json — full dataset · chips.json — chip summaries · benchmarks.csv — CSV export