Mistral Small 24B Instruct 2501 needs ~14.3 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q2_K quantization, expect ~9 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
7.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.4 tok/s
TTFT
56126 ms
Safe context
4K
Memory
19.6 GB / 12.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 | 4.0 tok/s | 26163 ms | 4K |
| Coding | F | Too heavy | 3.4 tok/s | 56126 ms | 4K |
| Agentic Coding | F | Too heavy | 2.6 tok/s | 108331 ms | 4K |
| Reasoning | F | Too heavy | 3.4 tok/s | 66330 ms | 4K |
| RAG | F | Too heavy | 2.6 tok/s | 135413 ms | 4K |
How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | F0 |
Q3_K_S | 3 | 11.8 GB | Low | F0 |
NVFP4 | 4 |
Copy-paste commands to run Mistral Small 24B Instruct 2501 on your machine.
Run
lms load hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf && lms server startUpgrade options
Intel Arc Pro B60 24GBBudget pickMakes 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.
~$599 MSRP
RTX 5090 32GBBiggest leapMakes 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.
~$1,999 MSRP
Intel Data Center GPU Max 1550 128GBBest valueMakes 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.
~$15,000 MSRP
Gaudi 3 128GBIntel upgradeMakes 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.
~$15,000 MSRP
Yes, Intel Arc Pro A60 12GB can run Mistral Small 24B Instruct 2501 at Q2_K quantization (Very compromised (needs ~1.5 GB host RAM)). The recommended Q4_K_M requires 19.6 GB which exceeds available memory, but at Q2_K it needs only 14.3 GB. Expected decode speed: 8.9 tok/s.
Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 19.6 GB at Q4_K_M quantization. On Intel Arc Pro A60 12GB, it fits at Q2_K using 14.3 GB.
The recommended quantization is Q4_K_M, but on Intel Arc Pro A60 12GB the best fitting quantization is Q2_K, which uses 14.3 GB.
On Intel Arc Pro A60 12GB, Mistral Small 24B Instruct 2501 achieves approximately 8.9 tokens per second decode speed with a time-to-first-token of 21763ms using Q2_K quantization.
For coding workloads, Mistral Small 24B Instruct 2501 on Intel Arc Pro A60 12GB receives a F grade with 3.4 tok/s and 4K context.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf-on-arc-pro-a60-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
13.4 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 14.6 GB | Medium | F0 |
Q5_K_M | 5 | 17.3 GB | High | F0 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
On Intel Arc Pro A60 12GB, Mistral Small 24B Instruct 2501 can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, 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.