Which LLMs Can You Run Locally on an Intel Arc Pro? A VRAM Guide
The first question everyone asks about running AI locally: which models will actually fit on my card? Here is the plain answer for the Intel Arc Pro range, without the marketing gloss.
The rule of thumb
A model quantized to 4-bit (Q4, the most common local format) needs roughly 0.55 to 0.6 GB of VRAM per billion parameters, plus headroom for context. Longer context windows eat more VRAM, so treat these as working guidance rather than hard limits, and leave a few GB spare.
What fits where
| Card | VRAM | Comfortable at Q4 | Examples |
|---|---|---|---|
| Arc Pro B60 24GB | 24GB | 7B to 14B, and many 20B-class MoE models | Llama 3.1 8B, Mistral 7B, Qwen2.5 14B, Phi-4, DeepSeek-R1 distill 14B |
| Arc Pro B70 32GB | 32GB | Up to ~34B | Qwen2.5 32B, DeepSeek-R1 distill 32B, Mistral Small, Gemma 2 27B |
| Arc Pro B60 Dual 48GB | 48GB (2x24 dual-die) | 70B-class quantized | Llama 3.3 70B (Q4), DeepSeek-R1 distill 70B, Qwen2.5 72B (tight, lower quant or short context) |
Three honest caveats
1. Context length costs VRAM. The figures above assume moderate context (4k to 8k tokens). Running 32k+ context pushes memory use up significantly; size one tier of headroom if long documents are your workload.
2. Quantization is a trade. Q4 models are very close to full quality for most uses, but if you need Q8 or full precision, halve the parameter counts above.
3. The 48GB card is dual-die. Two GPUs on one board. Inference frameworks with multi-GPU support (vLLM, IPEX-LLM, llama.cpp with splitting) use both; the motherboard slot must support PCIe x8/x8 bifurcation. Ask us before buying if unsure, we will tell you straight whether your board qualifies.
Why local at all?
Two reasons buyers give us: the data never leaves your premises (GDPR, client confidentiality, NHS/legal/finance data residency), and the maths. A one-off card versus a monthly cloud GPU bill crosses over fast; check your own numbers with our payback calculator.
All three cards are UK stock, dispatched from our Harlow workshop. Not sure which fits your workload? Email alex@destellotech.com with the model you want to run and you will get a straight answer from the founder, not a sales script.