Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 92%.
~$2,499 MSRP
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VOOZH | about |
Mixtral 8x22B needs ~93.8 GB but MacBook Pro M2 Max 32GB only has 23.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
70.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.5 tok/s
TTFT
76747 ms
Safe context
4K
Memory
93.8 GB / 23.0 GB
Offload
80%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 93.8 GB, but this setup only exposes 23.0 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.5 tok/s | 41862 ms | 4K |
| Coding | F | Too heavy | 2.5 tok/s | 76747 ms | 4K |
| Agentic Coding | F | Too heavy | 2.5 tok/s | 111632 ms | 4K |
| Reasoning | F | Too heavy | 2.5 tok/s | 90701 ms | 4K |
| RAG | F | Too heavy | 2.5 tok/s | 139540 ms | 4K |
How Mixtral 8x22B (141B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 55.0 GB | Low | F0 |
Q3_K_S | 3 | 69.1 GB | Low | F0 |
NVFP4 | 4 | 79.0 GB | Medium | F0 |
Q4_K_M | 4 | 86.0 GB | Medium | F0 |
Q5_K_M | 5 | 101.5 GB | High | F0 |
Q6_K | 6 | 115.6 GB | High | F0 |
Q8_0 | 8 | 150.9 GB | Very High | F0 |
F16 | 16 | 289.0 GB | Maximum | F0 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 92%.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 268%.
~$3,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$6,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$30,000 MSRP