Apple M4 Max 128GB vs Apple M5 Max 128GB for Local LLMs
Both have 96.0 GB, so they run the same models. The Apple M5 Max 128GB generates tokens about 12% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| Apple M4 Max 128GB | Apple M5 Max 128GB | |
|---|---|---|
| Memory | 128 GB unified | 128 GB unified |
| GPU-usable memory | 96.0 GB | 96.0 GB |
| Bandwidth | 546 GB/s | 614 GB/s |
| Architecture | Apple Silicon | Apple Silicon |
| Released | 2024-10 | 2026-03 |
| Llama 3.1 8B Q4_K_M | ~64 tok/s | ~72 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | Apple M4 Max 128GB | Apple M5 Max 128GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~64 tok/s | Fits ~72 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~36 tok/s | Fits ~41 tok/s |
| gpt-oss-20b | 13.9 GB | Fits ~107 tok/s | Fits ~116 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | Fits ~86 tok/s | Fits ~92 tok/s |
| Qwen 3 32B | 21.4 GB | Fits ~17 tok/s | Fits ~19 tok/s |
| Llama 3.3 70B | 43.2 GB | Fits ~8.0 tok/s | Fits ~9.0 tok/s |
| gpt-oss-120b | 62.0 GB | Fits ~83 tok/s | Fits ~90 tok/s |
Verdict
Same 128 GB and the same default GPU allocation, so the same models fit. The M5 Max has about 12% more bandwidth (614 vs 546 GB/s), so text generation is a little faster, and its GPU neural accelerators make prompt processing much faster, which matters for long documents and coding agents. A used M4 Max remains good value.
Which should you buy?
Choose the Apple M4 Max 128GB if…
- You can buy one used or discounted.
- Most of your prompts are short chats.
Choose the Apple M5 Max 128GB if…
- You use coding agents or long documents, where faster prompt processing matters.
- You are buying new.
- You want slightly faster generation.
Best quality and longest context on each card
The highest-quality quantization that fits fully at 8K context, and the longest context that fits at Q4_K_M with an FP16 cache.
| Model | Apple M4 Max 128GB: best quant | Apple M5 Max 128GB: best quant | Apple M4 Max 128GB: max context | Apple M5 Max 128GB: max context |
|---|---|---|---|---|
| Qwen 3.5 9B | FP16 / BF16 | FP16 / BF16 | 256K | 256K |
| Qwen 3 14B | FP16 / BF16 | FP16 / BF16 | 40K | 40K |
| gpt-oss-20b | MXFP4 (official) | MXFP4 (official) | 128K | 128K |
| Qwen 3.6 27B | FP16 / BF16 | FP16 / BF16 | 256K | 256K |
| Qwen 3 32B | FP16 / BF16 | FP16 / BF16 | 40K | 40K |
| Llama 3.3 70B | Q8_0 | Q8_0 | 128K | 128K |
Image generation
Best weight precision for image models with model swapping, at each model's default resolution. Image generation is also limited by compute, so the card with more tensor-core throughput finishes each image faster even when both fit.
| Model | Apple M4 Max 128GB | Apple M5 Max 128GB |
|---|---|---|
| SDXL 1.0 | FP16 / BF16 | FP16 / BF16 |
| FLUX.1 [dev] | FP16 / BF16 | FP16 / BF16 |
| Qwen-Image | FP16 / BF16 | FP16 / BF16 |
| Wan 2.2 A14B (T2V / I2V) | FP16 / BF16 | FP16 / BF16 |
Frequently asked questions
Is the Apple M4 Max 128GB or Apple M5 Max 128GB better for local LLMs?
Same 128 GB and the same default GPU allocation, so the same models fit. The M5 Max has about 12% more bandwidth (614 vs 546 GB/s), so text generation is a little faster, and its GPU neural accelerators make prompt processing much faster, which matters for long documents and coding agents. A used M4 Max remains good value.
Which is faster for LLM inference?
The Apple M5 Max 128GB, by about 12% for token generation on models that fit both (about 72 vs 64 tokens/s on Llama 3.1 8B Q4_K_M).
Which can run bigger models?
Neither: both have 96.0 GB usable, so they run the same models.