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 B570 10GB is Intel's entry Battlemage GPU, bringing the second-generation Xe HPG architecture at a $219 price point. Battlemage delivers significantly improved XMX engine throughput — 4,096 INT8 ops per clock — over Alchemist, translating to better LLM inference performance per dollar. The 10 GB of GDDR6 over PCIe 5 covers 7B models at Q4/Q8 and smaller models at FP16. It is a compelling budget option for users willing to work within the oneAPI software ecosystem.
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 natively | 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) | Very constrained | LTX Video 2B |
| Video Long (100f) | Won't fit | Wan Video 14B |
Architecture
Battlemage is Intel's second-generation Arc GPU architecture (Xe2-HPG), built on TSMC N4. It delivers significant performance-per-watt improvements over Alchemist with enhanced XMX engines and improved driver maturity.
AI Relevance
Better driver stability and improved XMX throughput make Battlemage more viable for AI inference than Alchemist. The Arc B580 (12 GB) is an increasingly popular budget option for local LLM experimentation via SYCL/oneAPI backends in llama.cpp.
購入アドバイス
制限付きでローカルAIに使用可能
上位50モデル中7モデルを実行可能（主に小規模）。大規模モデルには強い量子化が必要か、適合しません。
10.0 GB
VRAM
$219
希望小売価格
$22/GB
GBあたりのコスト
このGPUに最適なモデル
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 1 additional models that do not fit on the current setup.
もっと余裕が欲しいですか？ GTX 1080 Ti 11GB (11.0 GB VRAM) が次のステップアップです。
Chat
SQwen 3 8B 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
ACodeGeeX 4 9B 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
ACodestral Mamba 7B is a specialized fit for Agentic 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.
Reasoning
AGemma 4 E4B matches Reasoning 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.
RAG
ACodestral Mamba 7B is viable for RAG, 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.
Qwen 3.5 4BQwen 3.5 9BQwen 3 8B
Phi-4 Mini Reasoning 4B
Nemotron Nano 8B
Jina Embeddings v3
BGE M3Qwen3-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もう少しで届く
もう少しメモリがあれば動く高品質モデル
Image & Video Generation
23 of 52 models can generate images or video on your Intel Arc B570 10GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~3s | S |
| Stable Diffusion 1.5Image | 512×768 | ~6.1s | S |
| Realistic Vision v5.1Image | 512×768 | ~6.1s | S |
| DreamShaper 8Image | 512×768 | ~6.1s | S |
| LCM DreamShaper v7Image | 512×768 | ~1.8s | S |
| PixArt-SigmaImage | 256×256 | ~24.3s | S |
| FramePack I2VVideo | 256×256 | ~44.6s/frame | S |
| SDXL TurboImage | 512×512 | ~3s | S |
| SDXL LightningImage | 1024×1024 | ~9.1s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~24.3s | S |
| Playground v2.5Image | 256×256 | ~1m 26s | S |
| RealVisXL v5.0Image | 1024×1024 | ~27.3s | S |
| DreamShaper XLImage | 1024×1024 | ~27.3s | S |
| Juggernaut XL v9Image | 1024×1024 | ~27.3s | S |
| Animagine XL 3.1Image | 1024×1024 | ~27.3s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~27.3s | S |
| Animagine XL 4.0Image | 1024×1024 | ~27.3s | S |
| Illustrious XLImage | 1024×1024 | ~27.3s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~17.8s/frame | B |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~42.5s | B |
| Flux.2 Klein 4BImage | 256×256 | ~7.3s | B |
| LTX Video 2BVideo | 256×256 | ~21.1s/frame | D |
| KolorsImage | 256×256 | ~48.6s | D |
| Stable CascadeImage | 256×256 | ~2m 22s | F |
| AuraFlow v0.3Image | 256×256 | ~1m 49s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~2m 14s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~24.3s | F |
| CogVideoX 2BVideo | 256×256 | ~21.1s/frame | F |
| HunyuanVideoVideo | 256×256 | ~44.6s/frame | F |
| ChromaImage | 256×256 | ~24.3s | F |
| Z-Image TurboImage | 256×256 | ~25.1s | F |
| Flux.1 DevImage | 256×256 | ~1m 49s | F |
| Flux.1 SchnellImage | 256×256 | ~21.3s | F |
| LTX Video 13BVideo | 256×256 | ~44.6s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~2m 2s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~11.1s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~34.8s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~30.4s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~30.4s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~12.1s | F |
| Flux.1 Fill DevImage | 256×256 | ~1m 43s | F |
| Mochi 1 PreviewVideo | 256×256 | ~40.2s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~37.3s/frame | F |
| Helios 14BVideo | 256×256 | ~45.9s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~45.9s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~45.9s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~45.9s/frame | F |
| Qwen ImageImage | 256×256 | ~40.9s | F |
| Qwen Image EditImage | 256×256 | ~40.9s | F |
| Flux.2 DevImage | 256×256 | ~19m 10s | F |
| MAGI-1Video | 256×256 | ~57s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~1m 12s | 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
アップグレードオプション
GTX 1080 Ti 11GB次のステップUnlocks 1 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 30%.
〜$699 MSRP
Intel Arc Pro A60 12GBIntelアップグレードUnlocks 4 additional models that do not fit on the current setup.
〜$499 MSRP
Intel Arc Pro B60 24GBコスパ最良Unlocks 77 additional models that do not fit on the current setup.
〜$599 MSRP
Unlocks 122 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 319%.
〜$8,000 MSRP
Intel Arc B570 10GB (10 GB VRAM) can run these top models: Qwen 3.5 4B (score: 94/100), Qwen 3.5 9B (score: 93/100), Qwen 3 8B (score: 92/100). See the full compatibility list above.
Intel Arc B570 10GB has 10 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, Intel Arc B570 10GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on Intel Arc B570 10GB, we recommend CodeGeeX 4 9B. It achieves 40.9 tokens per second with 68K context window. CodeGeeX 4 9B 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 B570 10GB: GTX 1080 Ti 11GB, Intel Arc Pro A60 12GB. Upgrading would unlock larger models and faster inference speeds.
Flux.1 Dev requires around 24 GB of usable memory at FP16. With 10 GB, Intel Arc B570 10GB cannot run Flux natively. Consider quantized GGUF variants or the smaller Schnell model with aggressive offloading.
Intel Arc B570 10GB (10 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 B570 10GB can handle basic AI image generation with SDXL and SD 1.5. With 10 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 B570 10GB with 10 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 10 GB on Intel Arc B570 10GB, 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 B570 10GB, 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 B570 10GB 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 B570 10GB can still be useful.
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