Mistral Small 24B Instruct 2501 needs ~20.1 GB but MacBook Pro M1 Pro 16GB only has 11.5 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
8.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.4 tok/s
TTFT
44096 ms
Safe context
4K
Memory
20.1 GB / 11.5 GB
Offload
40%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 20.1 GB, but this setup only exposes 11.5 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 | 4.8 tok/s | 22193 ms | 4K |
| Coding | F | Too heavy | 4.4 tok/s | 44096 ms | 4K |
| Agentic Coding | F | Too heavy | 4.0 tok/s | 70472 ms | 4K |
| Reasoning | F | Too heavy | 4.4 tok/s | 52113 ms | 4K |
| RAG | F | Too heavy | 4.0 tok/s | 88090 ms | 4K |
How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | F0 |
Q3_K_S | 3 | 11.8 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.
~$799 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.
~$1,099 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.
~$1,099 MSRP
RTX 5090 32GBBiggest leapMakes 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.
~$1,999 MSRP
No, Mistral Small 24B Instruct 2501 requires more memory than MacBook Pro M1 Pro 16GB provides.
Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 20.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Small 24B Instruct 2501 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Pro 16GB, Mistral Small 24B Instruct 2501 achieves approximately 4.4 tokens per second decode speed with a time-to-first-token of 44096ms using Q4_K_M quantization.
For coding workloads, Mistral Small 24B Instruct 2501 on MacBook Pro M1 Pro 16GB receives a F grade with 4.4 tok/s and 4K context.
On MacBook Pro M1 Pro 16GB, Mistral Small 24B Instruct 2501 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf-on-m1-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
13.4 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 14.6 GB | Medium | F0 |
Q5_K_M | 5 | 17.3 GB | High | F0 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 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.
Not always. MacBook Pro M1 Pro 16GB 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.
