NVIDIA A100 40GBBudget pick1555 GB/s (+1009)
C
Raises estimated decode speed by about 161%.89.2 tok/s decode
Raises estimated decode speed by about 161%.
~$10,000 MSRP
Mistral Small 24B Instruct 2501 needs ~23.5 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~24 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
Runs well
Decode
34.2 tok/s
TTFT
5663 ms
Safe context
79K
Memory
23.5 GB / 34.6 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 | 23.5 tok/s | 4494 ms | 79K |
| Coding | C | Runs well | 23.5 tok/s | 8240 ms | 79K |
| Agentic Coding | C | Runs well | 23.5 tok/s | 11985 ms | 79K |
| Reasoning | C | Runs well | 23.5 tok/s | 9738 ms | 79K |
| RAG | C | Runs well | 23.5 tok/s | 14981 ms | 79K |
How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C45 |
Q3_K_S | 3 | 11.8 GB | Low | C46 |
NVFP4 | 4 |
Copy-paste commands to run Mistral Small 24B Instruct 2501 on your machine.
Run
lms load hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf && lms server startUpgrade options
Yes, MacBook Pro M4 Max 48GB can run Mistral Small 24B Instruct 2501 with a C grade (Runs well). Expected decode speed: 23.5 tok/s.
Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 23.5 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 M4 Max 48GB, Mistral Small 24B Instruct 2501 achieves approximately 23.5 tokens per second decode speed with a time-to-first-token of 8240ms using Q4_K_M quantization.
For coding workloads, Mistral Small 24B Instruct 2501 on MacBook Pro M4 Max 48GB receives a C grade with 23.5 tok/s and 79K context.
On MacBook Pro M4 Max 48GB, Mistral Small 24B Instruct 2501 can safely use up to 79K 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-m4-max-48gb" 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 |
| C47 |
Q4_K_M | 4 | 14.6 GB | Medium | C48 |
Q5_K_M | 5 | 17.3 GB | High | C49 |
Q6_K | 6 | 19.7 GB | High | C49 |
Q8_0Best for your GPU | 8 | 25.7 GB | Very High | C49 |
F16 | 16 | 49.2 GB | Maximum | F0 |
Not always. MacBook Pro M4 Max 48GB 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.