Ministral 3 8B needs ~10.1 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~48 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
Tight fit
Decode
48.2 tok/s
TTFT
4015 ms
Safe context
30K
Memory
10.1 GB / 12.0 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 | S | Runs well | 48.2 tok/s | 2190 ms | 30K |
| Coding | A | Tight fit | 48.2 tok/s | 4015 ms | 30K |
| Agentic Coding | A | Runs with offload (needs ~0.1 GB host RAM) | 35.2 tok/s | 7998 ms | 30K |
| Reasoning | A | Tight fit | 48.2 tok/s | 4745 ms | 30K |
| RAG | A | Runs with offload (needs ~0.1 GB host RAM) | 35.2 tok/s | 9998 ms | 30K |
How Ministral 3 8B (8B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A80 |
Q3_K_S | 3 | 3.9 GB | Low | A81 |
NVFP4 | 4 | 4.5 GB | Medium | A82 |
Q4_K_M | 4 | 4.9 GB | Medium | A82 |
Q5_K_M | 5 | 5.8 GB | High | A83 |
Q6_K | 6 | 6.6 GB | High | A83 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | A82 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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 |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 42.9 tok/s | |
| Qwen 3 14B | 14B | A | 15.5 tok/s | |
 Ministral 3 14B | 14B | A | 15.4 tok/s |
Yes, Intel Arc B580 12GB can run Ministral 3 8B with a A grade (Tight fit). Expected decode speed: 48.2 tok/s.
Ministral 3 8B (8B parameters) requires approximately 10.1 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 B580 12GB, Ministral 3 8B achieves approximately 48.2 tokens per second decode speed with a time-to-first-token of 4015ms using Q4_K_M quantization.
For coding workloads, Ministral 3 8B on Intel Arc B580 12GB receives a A grade with 48.2 tok/s and 30K context.
On Intel Arc B580 12GB, Ministral 3 8B can safely use up to 30K 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-b580-12gb" 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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