Ministral 3 8B needs ~11.3 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~54 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
54.2 tok/s
TTFT
3569 ms
Safe context
109K
Memory
11.3 GB / 24.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 54.2 tok/s | 1947 ms | 109K |
| Coding | A | Runs well | 54.2 tok/s | 3569 ms | 109K |
| Agentic Coding | A | Runs well | 54.2 tok/s | 5191 ms | 109K |
| Reasoning | A | Runs well | 54.2 tok/s | 4218 ms | 109K |
| RAG | A | Runs well | 54.2 tok/s | 6489 ms | 109K |
How Ministral 3 8B (8B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A75 |
Q3_K_S | 3 | 3.9 GB | Low | A75 |
NVFP4 | 4 | 4.5 GB | Medium | A76 |
Q4_K_M | 4 | 4.9 GB | Medium | A76 |
Q5_K_M | 5 | 5.8 GB | High | A76 |
Q6_K | 6 | 6.6 GB | High | A77 |
Q8_0 | 8 | 8.6 GB | Very High | A78 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | A80 |
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 |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 27.8 tok/s | |
| Qwen 3.5 27B | 27B | S | 16.1 tok/s | |
| Qwen 3.6 27B | 27B | S | 16.2 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 38.5 tok/s | |
| Qwen 3.5 9B | 9B | S | 48.2 tok/s |
Yes, Intel Arc Pro B60 24GB can run Ministral 3 8B with a A grade (Runs well). Expected decode speed: 54.2 tok/s.
Ministral 3 8B (8B parameters) requires approximately 11.3 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 Intel Arc Pro B60 24GB, Ministral 3 8B achieves approximately 54.2 tokens per second decode speed with a time-to-first-token of 3569ms using Q4_K_M quantization.
For coding workloads, Ministral 3 8B on Intel Arc Pro B60 24GB receives a A grade with 54.2 tok/s and 109K context.
On Intel Arc Pro B60 24GB, Ministral 3 8B can safely use up to 109K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/ministral-3-8b-on-arc-pro-b60-24gb" 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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