Arc A770 16GB vs Arc B580 for Local AI

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Which GPU is better for local AI workloads?

The A770 16GB carries 16GB VRAM and 560 GB/s bandwidth; the B580 has 12GB and 456 GB/s at $249 versus $349. For LLM capacity the A770's 16GB holds 14B at Q8 while the B580 stops at 12B. Driver maturity improved greatly on both by 2026; neither matches NVIDIA for tool compatibility.

Spec comparison: what actually differs

The table below is computed live from our hardware database. Positive deltas favor the Arc A770 16GB.

Specification	Arc A770 16GB	Arc B580	Difference
VRAM	16	12	+33%
Memory bandwidth	560	456	+23%
Memory type	GDDR6	GDDR6	—
Memory bus	256	192	—
TDP	225	190	+18%
Architecture	Alchemist	Battlemage	—
Launch MSRP	$349	$249	+40%
Street price	$349	$249	+40%

Specs from our sourced product database. See how we source data.

What about price?

Launch MSRP: $349 (A770 16GB) versus $249 (B580).

Which one should you buy for LLMs and image generation?

Intel · 2022

Arc A770 16GB

Buy a discounted A770 16GB for 14B-class models on a budget.

Full specs & benchmarks →

Intel · 2024

Arc B580

Buy the B580 for a current, cheaper, cooler (190W vs 225W) card.

Full specs & benchmarks →

Is it faster for LLM inference?

A770's 16GB + 560 GB/s is the stronger LLM combination if you find one cheap; production has wound down.

How does it handle image generation?

Both run SDXL via IPEX-UX; A770's capacity gives batching headroom.

Which AI models fit on each card?

Computed from our model VRAM database at Q4 quantization: Arc A770 16GB has 16 GB, Arc B580 has 12 GB.

Fit only on the Arc A770 16GB (16 GB)

Qwen3.8-27B ✓ Qwen3.5-27B ✓ Qwen3.6-27B ✓ Gemma 3 27B ✓ Wan 2.2 (A14B & TI2V-5B) ✓ OUI 1 ✓ Gemma 4 26B A4B IT ✓ Mistral Small 3.2 24B ✓ Devstral Small 2 24B Instruct ✓ Magistral-Small-2509 ✓

Fit on both cards

Gpt Oss 20B Maple Preview Ling Mini 2.0 Deepseek R1 Distill Qwen 14B Qwen3 14B Confucius4-T3PO WebWorld-14B ContextPilot 14B Phi 4 Phi 3 Medium Ministral 3 14B Instruct Ministral 3 14B Reasoning Gemma 3 12B Mellum 2.1 Thinking FLUX.1 dev Gemma 4 12B IT Ornith 1.5 9B OxCoder 9B Gemma 2 9B K2 Horizon 7B NeoHorse 1 9B Ministral 3 8B Reasoning Ministral 3 8B Instruct Granite 4.2 8B LFM2.5 8B A1B Qwen 3 8B ALoDLM 8B Deepseek R1 Distill Llama 8B Llama 3.1 8B Stable Diffusion 3.5 Large Gemma 4 E4B IT Edge0 8B A1B (preview) Ling-3.0-tiny Qwen2 5 7B ZGCM-1 7B Mistral 7B V03 Gemma 4 E2B IT K2 Horizon 3.7B Gemma 3 4B Ministral 3 3B Reasoning NeoHorse 1 4B Nanbeige4.2 3B Spark X2.5 4B Qwen3 4B Ministral 3 3B Instruct Granite 4.2 3B Stable Diffusion XL 1.0 Llama 3 2 3B Granite Swash 3B A600M LFM2.5 2.6B MiniCPM5 2B Qwen3 1 7B Spark X2.5 1.7B Whisper large-v3 Llama 3 2 1B K2 Horizon 0.9B Coqui XTTS-v2 Qwen3 0 6B GPT-X3 150M

Q4_K_M-equivalent sizes; context and quantization choices shift real limits. Check each model page for full quantization tables.

Frequently asked questions

A770 16GB or B580 for LLMs?

A770: 16GB holds 14B models at Q8 and 560 GB/s bandwidth beats B580's 456. B580 wins on price, availability, and efficiency.