Ministral 3 8B needs ~36.5 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~112 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
112.0 tok/s
TTFT
1729 ms
Safe context
262K
Memory
36.5 GB / 184.3 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 | 112.0 tok/s | 943 ms | 262K |
| Coding | A | Runs well | 112.0 tok/s | 1729 ms | 262K |
| Agentic Coding | A | Runs well | 112.0 tok/s | 2514 ms | 262K |
| Reasoning | A | Runs well | 112.0 tok/s | 2043 ms | 262K |
| RAG | A | Runs well | 112.0 tok/s | 3143 ms | 262K |
How Ministral 3 8B (8B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B68 |
Q3_K_S | 3 | 3.9 GB | Low | B68 |
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 | 8.1 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S |
Yes, Mac Studio M3 Ultra 256GB can run Ministral 3 8B with a A grade (Runs well). Expected decode speed: 112.0 tok/s.
Ministral 3 8B (8B parameters) requires approximately 36.5 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 Mac Studio M3 Ultra 256GB, Ministral 3 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.
For coding workloads, Ministral 3 8B on Mac Studio M3 Ultra 256GB receives a A grade with 112.0 tok/s and 262K context.
On Mac Studio M3 Ultra 256GB, 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-m3-ultra-256gb" 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 |
| B68 |
Q4_K_M | 4 | 4.9 GB | Medium | B68 |
Q5_K_M | 5 | 5.8 GB | High | B68 |
Q6_K | 6 | 6.6 GB | High | B68 |
Q8_0 | 8 | 8.6 GB | Very High | B68 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | B68 |
| 84.2 tok/s |
| Qwen 3.5 27B | 27B | S | 36.5 tok/s |
| Qwen 3.6 27B | 27B | S | 36.6 tok/s |
| Qwen 3.5 122B A10B | 122B | S | 22.4 tok/s |
Not always. Mac Studio M3 Ultra 256GB 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.