Magistral 7B needs ~9.7 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~17 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
17.1 tok/s
TTFT
11309 ms
Safe context
8K
Memory
9.7 GB / 17.3 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 | A | Runs well | 17.1 tok/s | 6168 ms | 8K |
| Coding | A | Runs well | 17.1 tok/s | 11309 ms | 8K |
| Agentic Coding | A | Runs well | 17.1 tok/s | 16449 ms | 8K |
| Reasoning | A | Runs well | 17.1 tok/s | 13365 ms | 8K |
| RAG | A | Runs well | 17.1 tok/s | 20561 ms | 8K |
How Magistral 7B (7B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A74 |
Q3_K_S | 3 | 3.4 GB | Low | A75 |
NVFP4 | 4 |
Copy-paste commands to run Magistral 7B on your machine.
Run
lms load Magistral-7B && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 13.3 tok/s | |
 Magistral Small 2507 | 24B | B | 3.8 tok/s |
Yes, MacBook Pro M3 24GB can run Magistral 7B with a A grade (Runs well). Expected decode speed: 17.1 tok/s.
Magistral 7B (7B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Magistral 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 24GB, Magistral 7B achieves approximately 17.1 tokens per second decode speed with a time-to-first-token of 11309ms using Q4_K_M quantization.
For coding workloads, Magistral 7B on MacBook Pro M3 24GB receives a A grade with 17.1 tok/s and 8K context.
On MacBook Pro M3 24GB, Magistral 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, 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/magistral-7b-on-m3-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| A75 |
Q4_K_M | 4 | 4.3 GB | Medium | A75 |
Q5_K_M | 5 | 5.0 GB | High | A76 |
Q6_K | 6 | 5.7 GB | High | A77 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A78 |
F16 | 16 | 14.3 GB | Maximum | F0 |
| Devstral Small 2 24B Instruct | 24B | B | 3.8 tok/s |
| Qwen 3 14B | 14B | S | 8.6 tok/s |
| Qwen 3 8B | 8B | S | 15 tok/s |
Not always. MacBook Pro M3 24GB 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.