Intel Arc B580 12GB vs RTX 3060 12GB for Local LLMs
Both have 12.0 GB, so they run the same models. The RTX 3060 12GB generates tokens about 3% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| Intel Arc B580 12GB | RTX 3060 12GB | |
|---|---|---|
| Memory | 12 GB | 12 GB |
| GPU-usable memory | 12.0 GB | 12.0 GB |
| Bandwidth | 456 GB/s | 360 GB/s |
| Architecture | Battlemage | Ampere |
| Released | 2024-12 | 2021-02 |
| Llama 3.1 8B Q4_K_M | ~42 tok/s | ~43 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | Intel Arc B580 12GB | RTX 3060 12GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~42 tok/s | Fits ~43 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~23 tok/s | Fits ~24 tok/s |
| gpt-oss-20b | 13.9 GB | 81% on GPU ~45 tok/s | 81% on GPU ~45 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | 60% on GPU ~29 tok/s | 60% on GPU ~30 tok/s |
| Qwen 3 32B | 21.4 GB | 53% on GPU ~3.5 tok/s | 53% on GPU ~3.5 tok/s |
| Llama 3.3 70B | 43.2 GB | Too small | Too small |
| gpt-oss-120b | 62.0 GB | Too small | Too small |
Verdict
Both have 12 GB, so they fit the same models. The B580 has more bandwidth (456 vs 360 GB/s) but Intel's software is less efficient, so real token speed ends up similar. The RTX 3060 works with every AI tool through CUDA; the B580 is the better new card if you are comfortable with llama.cpp's SYCL or Vulkan backends.
Which should you buy?
Choose the Intel Arc B580 12GB if…
- You want a new budget 12 GB card.
- You are happy to use llama.cpp's Vulkan or SYCL backends.
- You also game at 1440p.
Choose the RTX 3060 12GB if…
- You want every AI tool to work with CUDA.
- You generate images or train small models.
- You find one used at a good price.
Best quality and longest context on each card
The highest-quality quantization that fits fully at 8K context, and the longest context that fits at Q4_K_M with an FP16 cache.
| Model | Intel Arc B580 12GB: best quant | RTX 3060 12GB: best quant | Intel Arc B580 12GB: max context | RTX 3060 12GB: max context |
|---|---|---|---|---|
| Qwen 3.5 9B | Q8_0 | Q8_0 | 164K | 164K |
| Qwen 3 14B | Q4_K_M | Q4_K_M | 13K | 13K |
| gpt-oss-20b | Q3_K_M | Q3_K_M | – | – |
| Qwen 3.6 27B | Q2_K | Q2_K | – | – |
| Qwen 3 32B | Does not fit | Does not fit | – | – |
| Llama 3.3 70B | Does not fit | Does not fit | – | – |
Image generation
Best weight precision for image models with model swapping, at each model's default resolution. Image generation is also limited by compute, so the card with more tensor-core throughput finishes each image faster even when both fit.
| Model | Intel Arc B580 12GB | RTX 3060 12GB |
|---|---|---|
| SDXL 1.0 | FP16 / BF16 | FP16 / BF16 |
| FLUX.1 [dev] | GGUF Q6_K | GGUF Q6_K |
| Qwen-Image | Needs offloading | Needs offloading |
| Wan 2.2 A14B (T2V / I2V) | Needs offloading | Needs offloading |
Frequently asked questions
Is the Intel Arc B580 12GB or RTX 3060 12GB better for local LLMs?
Both have 12 GB, so they fit the same models. The B580 has more bandwidth (456 vs 360 GB/s) but Intel's software is less efficient, so real token speed ends up similar. The RTX 3060 works with every AI tool through CUDA; the B580 is the better new card if you are comfortable with llama.cpp's SYCL or Vulkan backends.
Which is faster for LLM inference?
The RTX 3060 12GB, by about 3% for token generation on models that fit both (about 43 vs 42 tokens/s on Llama 3.1 8B Q4_K_M).
Which can run bigger models?
Neither: both have 12.0 GB usable, so they run the same models.