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Mixtral 8x7B needs ~34.1 GB but MacBook Air M4 24GB only has 17.3 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
16.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.8 tok/s
TTFT
69226 ms
Safe context
4K
Memory
34.1 GB / 17.3 GB
Offload
50%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 34.1 GB, but this setup only exposes 17.3 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 | 2.6 tok/s | 40591 ms | 4K |
| Coding | F | Too heavy | 2.6 tok/s | 74417 ms | 4K |
| Agentic Coding | F | Too heavy | 2.6 tok/s | 108244 ms | 4K |
| Reasoning | F | Too heavy | 2.6 tok/s | 87948 ms | 4K |
| RAG | F | Too heavy | 2.6 tok/s | 135305 ms | 4K |
How Mixtral 8x7B (47B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.3 GB | Low | F0 |
Q3_K_S | 3 | 23.0 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
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.
~$1,099 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.
~$1,599 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.
~$2,499 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.
~$4,999 MSRP
| Medium |
| F0 |
Q4_K_M | 4 | 28.7 GB | Medium | F0 |
Q5_K_M | 5 | 33.8 GB | High | F0 |
Q6_K | 6 | 38.5 GB | High | F0 |
Q8_0 | 8 | 50.3 GB | Very High | F0 |
F16 | 16 | 96.4 GB | Maximum | F0 |
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.
