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 A730M 12GB is Intel's high-end Alchemist mobile GPU, targeting thin-and-light laptops that need discrete GPU performance for gaming and AI inference. With 12 GB of GDDR6 and 22 TFLOPS FP16, it can run 7B models at FP16 or 13B at Q4 quantization on-GPU, making it a capable option for laptop-based local LLM inference. The mobile form factor means power and thermal limits will constrain sustained inference throughput compared to desktop equivalents.
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) | Runs with offload | 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.
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10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。
12.0 GB
VRAM
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？ MacBook Pro M3 Pro 18GB (18.0 GB unified memory) が次のステップアップです。
Chat
SThis model is a direct match for chat. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
Coding
SThis model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
Agentic Coding
AThis model is still usable for agentic-coding, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
Reasoning
SThis model is a direct match for reasoning. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
RAG
AThis model is still usable for rag, but it is not the most specialized pick. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama.
Qwen 3.5 9BQwen 3 8BQwen 3.5 4B
Nemotron Nano 8B
Phi-4 Mini Reasoning 4B
Jina Embeddings v3Qwen 3 14B
BGE M3
Ministral 3 14BPhi-4-reasoning-plus 14BQwen3-Coder 30B A3B InstructQwen 3.5 397B A17BDevstral 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 30B A3BMistral Small 4 119B
Command A 111BQwen 2.5 VL 72B
GPT-OSS 120BNemotron 3 Nano 30BQwen3-Coder-NextDevstral 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 671B
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
24 of 52 models can generate images or video on your Intel Arc A730M 12GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~2.9s | S |
| Stable Diffusion 1.5Image | 512×768 | ~5.8s | S |
| Realistic Vision v5.1Image | 512×768 | ~5.8s | S |
| DreamShaper 8Image | 512×768 | ~5.8s | S |
| LCM DreamShaper v7Image | 512×768 | ~1.7s | S |
| PixArt-SigmaImage | 256×256 | ~1m 44s | S |
| FramePack I2VVideo | 256×256 | ~42.4s/frame | S |
| SDXL TurboImage | 512×512 | ~2.9s | S |
| SDXL LightningImage | 1024×1024 | ~8.7s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~23.1s | S |
| Playground v2.5Image | 1024×1024 | ~34.7s | S |
| RealVisXL v5.0Image | 1024×1024 | ~26s | S |
| DreamShaper XLImage | 1024×1024 | ~26s | S |
| Juggernaut XL v9Image | 1024×1024 | ~26s | S |
| Animagine XL 3.1Image | 1024×1024 | ~26s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~26s | S |
| Animagine XL 4.0Image | 1024×1024 | ~26s | S |
| Illustrious XLImage | 1024×1024 | ~26s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~16.9s/frame | A |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~40.5s | A |
| Flux.2 Klein 4BImage | 256×256 | ~15.6s | A |
| LTX Video 2BVideo | 256×256 | ~20.1s/frame | B |
| KolorsImage | 256×256 | ~46.2s | B |
| Stable CascadeImage | 1024×1024 | ~57.8s | D |
| AuraFlow v0.3Image | 256×256 | ~1m 44s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~2m 7s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~23.1s | F |
| CogVideoX 2BVideo | 256×256 | ~20.1s/frame | F |
| HunyuanVideoVideo | 256×256 | ~42.4s/frame | F |
| ChromaImage | 256×256 | ~23.1s | F |
| Z-Image TurboImage | 256×256 | ~23.9s | F |
| Flux.1 DevImage | 256×256 | ~1m 44s | F |
| Flux.1 SchnellImage | 256×256 | ~20.2s | F |
| LTX Video 13BVideo | 256×256 | ~42.4s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~1m 56s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~10.5s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~33.1s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~29s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~29s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~11.6s | F |
| Flux.1 Fill DevImage | 256×256 | ~1m 38s | F |
| Mochi 1 PreviewVideo | 256×256 | ~38.2s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~35.5s/frame | F |
| Helios 14BVideo | 256×256 | ~43.7s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~43.7s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~43.7s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~43.7s/frame | F |
| Qwen ImageImage | 256×256 | ~38.9s | F |
| Qwen Image EditImage | 256×256 | ~38.9s | F |
| Flux.2 DevImage | 256×256 | ~18m 14s | F |
| MAGI-1Video | 256×256 | ~54.2s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~1m 9s | 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
Unlocks 1 additional models that do not fit on the current setup.
〜$1,999 MSRP
Intel Arc Pro B50 16GBIntelアップグレードUnlocks 37 additional models that do not fit on the current setup.
〜$399 MSRP
Intel Arc Pro B60 24GBコスパ最良Unlocks 73 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 19%.
〜$599 MSRP
Unlocks 118 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 383%.
〜$8,000 MSRP
Intel Arc A730M 12GB (12 GB VRAM) can run these top models: Qwen 3.5 9B (score: 95/100), Qwen 3 8B (score: 94/100), Qwen 3.5 4B (score: 92/100). See the full compatibility list above.
Intel Arc A730M 12GB has 12 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, Intel Arc A730M 12GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on Intel Arc A730M 12GB, we recommend Qwen 3.5 9B. It achieves 32.2 tokens per second with 32K context window. This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
There are 4 upgrade path(s) from Intel Arc A730M 12GB: MacBook Pro M3 Pro 18GB, Intel Arc Pro B50 16GB. Upgrading would unlock larger models and faster inference speeds.
Intel Arc A730M 12GB can run Flux.1 Dev with sequential offloading or at a lower precision (FP8/NF4). The Schnell variant is faster and fits more easily. For best results, use ComfyUI with model offloading enabled.
Intel Arc A730M 12GB (12 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.
Intel Arc A730M 12GB is good for AI image generation. It handles SDXL and SD 3.5 well, and can run Flux with some optimization. 12 GB of usable memory is sufficient for most image generation workflows at standard resolutions.
Qwen 3.5 27B does not fit on Intel Arc A730M 12GB with 12 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 12 GB on Intel Arc A730M 12GB, 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 A730M 12GB, 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 A730M 12GB 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 A730M 12GB can still be useful.
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