Mistral Small 3.2 24B Instruct 2506 needs ~22.2 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~27 tok/s.
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
Fit status
Tight fit
Decode
27.3 tok/s
TTFT
7102 ms
Safe context
37K
Memory
22.2 GB / 25.9 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 17.6 tok/s | 5985 ms | 37K |
| Coding | C | Tight fit | 27.3 tok/s | 7102 ms | 37K |
| Agentic Coding | C | Runs with offload | 27.3 tok/s | 10330 ms | 37K |
| Reasoning | C | Tight fit | 27.3 tok/s | 8394 ms | 37K |
| RAG | C | Runs with offload | 27.3 tok/s | 12913 ms | 37K |
How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C48 |
Q3_K_S | 3 | 11.8 GB | Low | C49 |
NVFP4 | 4 | 13.4 GB | Medium | C50 |
Q4_K_M | 4 | 14.6 GB | Medium | C50 |
Q5_K_M | 5 | 17.3 GB | High | C50 |
Q6_KBest for your GPU | 6 | 19.7 GB | High | C49 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
Copy-paste commands to run Mistral Small 3.2 24B Instruct 2506 on your machine.
Run
lms load hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf && lms server startUpgrade-Optionen
Raises estimated decode speed by about 25%.
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Adds memory headroom for longer context windows and future model growth.
ca. $2,999 MSRP
Yes, MacBook Pro M4 Max 36GB can run Mistral Small 3.2 24B Instruct 2506 with a C grade (Tight fit). Expected decode speed: 27.3 tok/s.
Mistral Small 3.2 24B Instruct 2506 (24B parameters) requires approximately 22.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Small 3.2 24B Instruct 2506 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 36GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 27.3 tokens per second decode speed with a time-to-first-token of 7102ms using Q4_K_M quantization.
For coding workloads, Mistral Small 3.2 24B Instruct 2506 on MacBook Pro M4 Max 36GB receives a C grade with 27.3 tok/s and 37K context.
On MacBook Pro M4 Max 36GB, Mistral Small 3.2 24B Instruct 2506 can safely use up to 37K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M4 Max 36GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf-on-m4-max-36gb" 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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