Mistral Nemo 12B needs ~8.8 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q2_K quantization, expect ~30 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
3.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
13.0 tok/s
TTFT
14934 ms
Safe context
4K
Memory
11.5 GB / 8.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 16.4 tok/s | 6427 ms | 4K |
| Coding | F | Too heavy | 13.0 tok/s | 14934 ms | 4K |
| Agentic Coding | F | Too heavy | 8.6 tok/s | 32616 ms | 4K |
| Reasoning | F | Too heavy | 13.0 tok/s | 17649 ms | 4K |
| RAG | F | Too heavy | 8.6 tok/s | 40770 ms | 4K |
How Mistral Nemo 12B (12B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 4.7 GB | Low | B65 |
Q3_K_S | 3 | 5.9 GB | Low | F0 |
NVFP4 | 4 | 6.7 GB | Medium | F0 |
Q4_K_M | 4 | 7.3 GB | Medium | F0 |
Q5_K_M | 5 | 8.6 GB | High | F0 |
Q6_K | 6 | 9.8 GB | High | F0 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Copy-paste commands to run Mistral Nemo 12B on your machine.
Run
ollama run mistral-nemoアップグレードオプション
Intel Arc B570 10GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 29%.
〜$219 MSRP
Intel Arc B580 12GBコスパ重視Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$249 MSRP
Intel Arc A770 16GBIntelアップグレードMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$349 MSRP
Yes, Intel Arc A580 8GB can run Mistral Nemo 12B at Q2_K quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 11.5 GB which exceeds available memory, but at Q2_K it needs only 8.8 GB. Expected decode speed: 29.9 tok/s.
Mistral Nemo 12B (12B parameters) requires approximately 11.5 GB at Q4_K_M quantization. On Intel Arc A580 8GB, it fits at Q2_K using 8.8 GB.
The recommended quantization is Q4_K_M, but on Intel Arc A580 8GB the best fitting quantization is Q2_K, which uses 8.8 GB.
On Intel Arc A580 8GB, Mistral Nemo 12B achieves approximately 29.9 tokens per second decode speed with a time-to-first-token of 6474ms using Q2_K quantization.
For coding workloads, Mistral Nemo 12B on Intel Arc A580 8GB receives a F grade with 13.0 tok/s and 4K context.
On Intel Arc A580 8GB, Mistral Nemo 12B can safely use up to 11K tokens of context at Q2_K quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
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