Ministral 3 8B needs ~22.7 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~83 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
82.6 tok/s
TTFT
2344 ms
Safe context
262K
Memory
22.7 GB / 92.2 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 | 82.6 tok/s | 1279 ms | 262K |
| Coding | A | Runs well | 82.6 tok/s | 2344 ms | 262K |
| Agentic Coding | A | Runs well | 82.6 tok/s | 3409 ms | 262K |
| Reasoning | A | Runs well | 82.6 tok/s | 2770 ms | 262K |
| RAG | A | Runs well | 82.6 tok/s | 4262 ms | 262K |
How Ministral 3 8B (8B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B70 |
Q3_K_S | 3 | 3.9 GB | Low | B70 |
NVFP4 | 4 |
Copy-paste commands to run Ministral 3 8B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "mistralai/Ministral-3-8B-Instruct-2512" \
--hf-file "Ministral-3-8B-Instruct-2512-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 7.5 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S |
Yes, MacBook Pro M4 Max 128GB can run Ministral 3 8B with a A grade (Runs well). Expected decode speed: 82.6 tok/s.
Ministral 3 8B (8B parameters) requires approximately 22.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 3 8B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 128GB, Ministral 3 8B achieves approximately 82.6 tokens per second decode speed with a time-to-first-token of 2344ms using Q4_K_M quantization.
For coding workloads, Ministral 3 8B on MacBook Pro M4 Max 128GB receives a A grade with 82.6 tok/s and 262K context.
On MacBook Pro M4 Max 128GB, Ministral 3 8B can safely use up to 262K tokens of context. The model's official context limit is 262K, 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/ministral-3-8b-on-m4-max-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| B70 |
Q4_K_M | 4 | 4.9 GB | Medium | B70 |
Q5_K_M | 5 | 5.8 GB | High | B70 |
Q6_K | 6 | 6.6 GB | High | B70 |
Q8_0 | 8 | 8.6 GB | Very High | B70 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | A71 |
| 52 tok/s |
| Qwen 3.5 27B | 27B | S | 36.1 tok/s |
| Qwen 3.6 27B | 27B | S | 36.2 tok/s |
| Qwen 3.5 122B A10B | 122B | S | 12.5 tok/s |
Not always. MacBook Pro M4 Max 128GB 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.