GPU guide
Intel Arc B580 for AI: what it actually runs
12 GB of VRAM at 456 GB/s — a strong value pick for running local chat and coding models, as long as you're comfortable outside NVIDIA's CUDA ecosystem.
AI suitability
At Q4_K_M with an 8K context this card comfortably fits 7 of the 12 models in our reference list; at the near-lossless Q8_0, 6. The largest comfortable Q4 fit is Qwen2.5 14B. For LLM inference the number that matters most is memory bandwidth, because generating each token means reading the whole model — on the Intel Arc B580 that puts the theoretical ceiling around 101 tokens/second on Llama 3.1 8B at Q4_K_M, with real-world throughput below that.
Software & ecosystem — the Intel Arc caveat
Intel Arc runs local LLMs via the IPEX-LLM runtime and the Vulkan/SYCL llama.cpp backends. Chat and coding models work well and improve with almost every driver release, but this is the youngest of the three ecosystems — expect occasional driver quirks and thinner support for image generation and fine-tuning than CUDA. For an inexpensive way to run 7B–14B chat models locally it punches above its price; for a daily production training box, NVIDIA is still the safe default.
Which models fit the Intel Arc B580?
Computed at an 8K context (or the model's own cap). ✓ fits comfortably (≤95% of 12 GB) · ~ tight · ✗ doesn't fit. Every model links to its own guide.
| Model | Q4_K_M | Q8_0 |
|---|---|---|
| Llama 3.2 1B | 1.6 GB ✓ | 2.0 GB ✓ |
| Llama 3.2 3B | 3.4 GB ✓ | 4.7 GB ✓ |
| Mistral 7B | 5.9 GB ✓ | 9.0 GB ✓ |
| Qwen2.5 7B | 5.3 GB ✓ | 8.5 GB ✓ |
| Llama 3.1 8B | 6.5 GB ✓ | 10.1 GB ✓ |
| Gemma 2 9B | 7.4 GB ✓ | 11.4 GB ~ |
| Qwen2.5 14B | 10.5 GB ✓ | 16.8 GB ✗ |
| Gemma 2 27B | 19.6 GB ✗ | 31.7 GB ✗ |
| Qwen2.5 32B | 21.7 GB ✗ | 36.0 GB ✗ |
| DeepSeek-R1 32B | 21.7 GB ✗ | 36.0 GB ✗ |
| Llama 3.3 70B | 44.7 GB ✗ | 76.0 GB ✗ |
| Qwen2.5 72B | 45.9 GB ✗ | 78.1 GB ✗ |
Electricity
The board is rated at 190 W. Run it under sustained load 8 hours a day and that's about 46 kWh a month — roughly $6/month at an example rate of $0.12/kWh (set your own tariff in the workspace). Idle and light chat draw far less; the figure above is the worst case, not the typical bill.
Alternatives
16 GB · 560 GB/s · 225 W
Step up — 16 GB clears the 12 GB ceiling on bigger models.
See this card →12 GB · 360 GB/s · 170 W
The 12 GB NVIDIA peer — slower memory but CUDA's mature tooling.
See this card →16 GB · 288 GB/s · 190 W
AMD's 16 GB budget card if you want more room than 12 GB.
See this card →Compare any two of these head-to-head — speed on the same model, cost, power — in GPU Compare, or get a pick for your budget in the Recommendation Wizard.
Sizing something specific? Common mistakes when sizing VRAM covers the traps — including why a card with more memory can be the slower one.
New to GPU specifications? What actually matters, in what order.
Common questions
Is the Intel Arc B580 good for AI and local LLMs?
Within its tier and ecosystem: with 12 GB of VRAM and 456 GB/s of bandwidth it fits 7 of the 12 models in our list at Q4_K_M with an 8K context, the largest being Qwen2.5 14B. It runs text-generation models well through IPEX-LLM and Vulkan; CUDA-only fine-tuning and image tooling need extra setup.
How much electricity does a Intel Arc B580 use?
The board is rated at 190 W. Under sustained load 8 hours a day that is about 46 kWh a month — roughly $6/month at $0.12/kWh. Idle draw is far lower.
Consumer cards, ranked by VRAM
Gaming cards — the cheapest route to VRAM. No ECC memory, and on NVIDIA no NVLink from Ada onward. Every card in this tier, smallest memory first — the one you are reading is highlighted. Cards shown without a memory figure are not in our calculation engine yet.