Arc B580 vs GeForce RTX 4060 for Local AI

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Which GPU is better for local AI workloads?

The B580's 12GB fits models the 4060's 8GB cannot (12B LLMs at Q4, SDXL more comfortably). The 4060's 272 GB/s trails the B580's 456 GB/s on paper, but real-world AI on Arc depends on IPEX-UX/Vulkan paths with rougher edges than CUDA. For tinkerers the B580 is remarkable value; for reliability the 4060 wins.

Spec comparison: what actually differs

The table below is computed live from our hardware database. Positive deltas favor the Arc B580.

Specification	Arc B580	GeForce RTX 4060	Difference
VRAM	12	8	+50%
Memory bandwidth	456	272	+68%
Memory type	GDDR6	GDDR6	—
Memory bus	192	128	—
TDP	190	115	+65%
CUDA cores	—	3072	—
Architecture	Battlemage	Ada Lovelace	—
Launch MSRP	$249	$299	-17%
Street price	$249	$299	-17%

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

What about price?

Launch MSRP: $249 (B580) versus $299 (4060).

Which one should you buy for LLMs and image generation?

Intel · 2024

Arc B580

Buy the B580 if you enjoy tweaking and want maximum VRAM per dollar.

Full specs & benchmarks →

NVIDIA · 2023

GeForce RTX 4060

Buy the 4060 if you want everything to work first try.

Full specs & benchmarks →

Is it faster for LLM inference?

llama.cpp Vulkan/OpenCL runs on Arc and holds 12B models in 12GB; expect more setup friction and community workarounds than NVIDIA.

How does it handle image generation?

SDXL runs via IPEX-UX/ComfyUI on Arc; workable but slower to set up and update. NVIDIA remains plug-and-play.

Which AI models fit on each card?

Computed from our model VRAM database at Q4 quantization: Arc B580 has 12 GB, GeForce RTX 4060 has 8 GB.

Fit only on the Arc B580 (12 GB)

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 ✓

Fit on both cards

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

Is the Arc B580 usable for local LLMs?

Yes via llama.cpp's Vulkan backend; 12GB holds 12B models at Q4. Setup is more manual than CUDA and performance depends on driver/runtime versions.

B580 vs 4060 — more VRAM or better software?

B580 for capacity (12 vs 8GB, $50 cheaper). 4060 for the CUDA ecosystem and predictable performance across every AI tool.