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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 | Detail |
|---|---|---|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
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.
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
AJust out of reach
High-quality models that need a bit more memory
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 v7 |
Upgrade paths
See what you unlock with more powerful hardware
Upgrade options
Unlocks 1 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 30%.
~$699 MSRP
Unlocks 4 additional models that do not fit on the current setup.
~$499 MSRP
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
Compare with similar
| Won’t fit |
| Llama 3.1 70B Q4 |
| — |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~24.3s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~1m 49s per image |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 | ~~2m 14s per image |
| Video Short (25f) | Very constrained | LTX Video 2B | ~~21.1s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~1m 2s/frame |
Codestral 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.
| 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.
Buying advice
Usable for local AI with limits
Can run 7 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.
10.0 GB
VRAM
$219
MSRP
$22/GB
Cost per GB VRAM
Best models for this GPU
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.
Want more headroom? GTX 1080 Ti 11GB (11.0 GB VRAM) is the next step up.
