Intel
Operating mode
Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
The Arc A580 8GB fills the mid-tier gap in Intel's Alchemist lineup, offering 8 GB of GDDR6 with a notably high 512 GB/s memory bandwidth for its class. The bandwidth matches the flagship A770 16GB, making it faster at decode than the specs alone suggest for models that fit in 8 GB. At $179 it is a competitive option for 7B model inference at Q4, and its SYCL support in llama.cpp enables full GPU acceleration without CPU offloading for common models.
Beyond LLMs
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model |
|---|---|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 |
| LLM Large (70B) | Won’t fit | Llama 3.1 70B Q4 |
| Image Gen (SDXL) | Runs with sequential offload | SDXL 1.0 FP16 |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 |
| Video Short (25f) | Won't fit | LTX Video 2B |
| Video Long (100f) | Won't fit | Wan Video 14B |
Architecture
Alchemist is Intel's first discrete GPU architecture under the Arc brand, using Xe-HPG cores manufactured on TSMC's N6 process. It features XMX (Xe Matrix Extensions) engines for AI acceleration.
AI Relevance
XMX engines provide some AI inference acceleration via oneAPI/SYCL. However, the software ecosystem for LLM inference on Intel Arc is still developing, with limited runtime support compared to CUDA.
购买建议
有限制地可用于本地 AI
可运行 50 个顶级模型中的 7 个，主要是较小的模型。较大模型需要强量化或无法适配。
8.0 GB
VRAM
$179
建议零售价
$22/GB
每 GB VRAM 成本
最适合此 GPU 的模型
What will limit you first
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Best upgrade itinerary
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Unlocks 33 additional models that do not fit on the current setup.
想要更多余量？ RTX 3080 10GB (10.0 GB VRAM) 是下一步升级选择。
Chat
SQwen 3.5 4B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Coding
ACodestral Mamba 7B is a specialized fit for Coding. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Agentic Coding
AGemma 4 E2B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Reasoning
ACodestral Mamba 7B is viable for Reasoning, but is not the most specialized choice. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
RAG
AGranite 4.1 3B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Qwen 3.5 4B
Phi-4 Mini Reasoning 4B
Jina Embeddings v3Qwen 3.5 9B
BGE M3Qwen 3 8B
Nemotron Nano 8BQwen3-Coder 30B A3B InstructQwen 3.5 397B A17B
Devstral 2 123B Instruct
Kimi K2.5Kimi K2.6
DeepSeek V4 ProQwen 3.5 27BQwen 3.6 27BQwen 3.5 122B A10BQwen3-VL 30B A3B InstructQwen 3.6 35B A3BDeepSeek V4 FlashQwen 3.5 35B A3BMagistral Small 2507Devstral Small 2 24B InstructQwen 3 32BQwen 3 14BQwen 3 30B A3BMistral Small 4 119B
Command A 111BQwen 2.5 VL 72B
GPT-OSS 120BNemotron 3 Nano 30BQwen3-Coder-NextPhi-4-reasoning-plus 14BDevstral Small 1.1
GLM-5.1Pixtral Large 124BGLM-5DeepSeek V3.2GPT-OSS 20BQwen 3 235B A22BQwen3-Coder 480B A35B InstructNemotron Cascade 2 30B A3B
Gemma 4 31BLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671BMinistral 3 14B
EXAONE 4.0 32BGemma 4 26B A4B触手可及
高质量模型，只需稍多一点内存
Image & Video Generation
21 of 52 models can generate images or video on your Intel Arc A580 8GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~5.3s | S |
| Stable Diffusion 1.5Image | 512×768 | ~10.6s | S |
| Realistic Vision v5.1Image | 512×768 | ~10.6s | S |
| DreamShaper 8Image | 512×768 | ~10.6s | S |
| LCM DreamShaper v7Image | 512×768 | ~3.2s | S |
| PixArt-SigmaImage | 256×256 | ~42.4s | S |
| FramePack I2VVideo | 256×256 | ~1m 18s/frame | A |
| SDXL TurboImage | 256×256 | ~14.1s | A |
| SDXL LightningImage | 256×256 | ~42.2s | B |
| Stable Diffusion XL 1.0Image | 256×256 | ~1m 53s | B |
| Playground v2.5Image | 256×256 | ~1m 4s | B |
| RealVisXL v5.0Image | 256×256 | ~2m 7s | B |
| DreamShaper XLImage | 256×256 | ~2m 7s | B |
| Juggernaut XL v9Image | 256×256 | ~2m 7s | B |
| Animagine XL 3.1Image | 256×256 | ~2m 7s | B |
| Pony Diffusion V6 XLImage | 256×256 | ~2m 7s | B |
| Animagine XL 4.0Image | 256×256 | ~2m 7s | B |
| Illustrious XLImage | 256×256 | ~2m 7s | B |
| Wan Video 2.1 1.3BVideo | 256×256 | ~31s/frame | D |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~1m 14s | D |
| Flux.2 Klein 4BImage | 256×256 | ~12.7s | D |
| LTX Video 2BVideo | 256×256 | ~36.8s/frame | F |
| KolorsImage | 256×256 | ~1m 25s | F |
| Stable CascadeImage | 256×256 | ~1m 46s | F |
| AuraFlow v0.3Image | 256×256 | ~3m 11s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~3m 53s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~42.4s | F |
| CogVideoX 2BVideo | 256×256 | ~36.8s/frame | F |
| HunyuanVideoVideo | 256×256 | ~1m 18s/frame | F |
| ChromaImage | 256×256 | ~42.4s | F |
| Z-Image TurboImage | 256×256 | ~43.7s | F |
| Flux.1 DevImage | 256×256 | ~3m 11s | F |
| Flux.1 SchnellImage | 256×256 | ~37.1s | F |
| LTX Video 13BVideo | 256×256 | ~1m 18s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~3m 32s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~19.3s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~1m 1s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~53.1s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~53.1s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~21.2s | F |
| Flux.1 Fill DevImage | 256×256 | ~3m 0s | F |
| Mochi 1 PreviewVideo | 256×256 | ~1m 10s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~1m 5s/frame | F |
| Helios 14BVideo | 256×256 | ~1m 20s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~1m 20s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~1m 20s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~1m 20s/frame | F |
| Qwen ImageImage | 256×256 | ~1m 11s | F |
| Qwen Image EditImage | 256×256 | ~1m 11s | F |
| Flux.2 DevImage | 256×256 | ~33m 26s | F |
| MAGI-1Video | 256×256 | ~1m 40s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~2m 6s | F |
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
Upgrade paths
See what you unlock with more powerful hardware
升级选项
RTX 3080 10GB下一步升级Unlocks 33 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 38%.
~$699 MSRP
Intel Arc B580 12GBIntel 升级Unlocks 37 additional models that do not fit on the current setup.
~$249 MSRP
Unlocks 74 additional models that do not fit on the current setup.
~$329 MSRP
Unlocks 155 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 245%.
~$8,000 MSRP
Intel Arc A580 8GB (8 GB VRAM) can run these top models: Qwen 3.5 4B (score: 95/100), Phi-4 Mini Reasoning 4B (score: 92/100), Jina Embeddings v3 (score: 84/100). See the full compatibility list above.
Intel Arc A580 8GB has 8 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, Intel Arc A580 8GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on Intel Arc A580 8GB, we recommend Codestral Mamba 7B. It achieves 67.6 tokens per second with 67K context window. Codestral Mamba 7B is a specialized fit for Coding. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
There are 4 upgrade path(s) from Intel Arc A580 8GB: RTX 3080 10GB, Intel Arc B580 12GB. Upgrading would unlock larger models and faster inference speeds.
Flux.1 Dev requires around 24 GB of usable memory at FP16. With 8 GB, Intel Arc A580 8GB cannot run Flux natively. Consider quantized GGUF variants or the smaller Schnell model with aggressive offloading.
Intel Arc A580 8GB (8 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. For video, video generation is limited by available memory. Check the AI Capability Matrix above for detailed compatibility.
Intel Arc A580 8GB can handle basic AI image generation with SDXL and SD 1.5. With 8 GB of usable memory, larger models like Flux will need quantization or offloading. Best suited for standard resolution (512-1024px) generation.
Qwen 3.5 27B does not fit on Intel Arc A580 8GB with 8 GB. However, Qwen 3.5 9B at Q4 (5.5 GB) or Q5 (6.5 GB) runs well on your GPU. The 4B variant fits at Q8 for near-lossless quality.
With 8 GB on Intel Arc A580 8GB, use Q4_K_M for 8B models and Q4_K_M with tight context for 14B models. Q5_K_M is a good middle ground when the model fits. For the best quality-to-size ratio, Q4_K_M is the most popular choice.
On Intel Arc A580 8GB, capacity is usually the first gate: if the model does not fit, bandwidth does not matter. But once a model fits, memory bandwidth is what largely determines tokens per second. In practice, you want enough memory to fit the model plus headroom, then as much bandwidth as your budget allows.
Intel Arc A580 8GB can be attractive on memory-per-dollar, but CUDA still has the broadest support across runtimes, kernels, guides, and community-tested local AI workflows. If your priority is the easiest setup and widest model compatibility, NVIDIA remains the safer choice. If your priority is value and you are comfortable with a narrower software stack, Intel Arc A580 8GB can still be useful.
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