Mistral Small 4 119B needs ~82.3 GB but MacBook Pro M1 Pro 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
59.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.0 tok/s
TTFT
48983 ms
Safe context
4K
Memory
82.3 GB / 23.0 GB
Offload
70%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 82.3 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 | 4.0 tok/s | 26718 ms | 4K |
| Coding | F | Too heavy | 3.6 tok/s | 53269 ms | 4K |
| Agentic Coding | F | Too heavy | 4.0 tok/s | 71247 ms | 4K |
| Reasoning | F | Too heavy | 4.0 tok/s | 57889 ms | 4K |
| RAG | F | Too heavy | 4.0 tok/s | 89059 ms | 4K |
How Mistral Small 4 119B (119B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 46.4 GB | Low | F0 |
Q3_K_S | 3 | 58.3 GB | Low | F0 |
NVFP4 | 4 | 66.6 GB | Medium | F0 |
Q4_K_M | 4 | 72.6 GB | Medium | F0 |
Q5_K_M | 5 | 85.7 GB | High | F0 |
Q6_K | 6 | 97.6 GB | High | F0 |
Q8_0 | 8 | 127.3 GB | Very High | F0 |
F16 | 16 | 244.0 GB | Maximum | F0 |
アップグレードオプション
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.
〜$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.
〜$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.
〜$15,000 MSRP
No, Mistral Small 4 119B requires more memory than MacBook Pro M1 Pro 32GB provides.
Mistral Small 4 119B (119B parameters) requires approximately 82.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Small 4 119B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Pro 32GB, Mistral Small 4 119B achieves approximately 3.6 tokens per second decode speed with a time-to-first-token of 53269ms using Q4_K_M quantization.
For coding workloads, Mistral Small 4 119B on MacBook Pro M1 Pro 32GB receives a F grade with 3.6 tok/s and 4K context.
On MacBook Pro M1 Pro 32GB, Mistral Small 4 119B can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
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.
Not always. MacBook Pro M1 Pro 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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<iframe src="https://willitrunai.com/embed/mistral-small-4-119b-on-m1-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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