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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 A750 8GB is Intel's mid-range Alchemist GPU offering solid AI inference at a budget price. It supports llama.cpp's SYCL backend via the oneAPI toolkit, enabling GPU-accelerated LLM inference on Linux and Windows. With 8 GB of GDDR6 and 21 TFLOPS FP16, it can handle 7B parameter models at Q4 quantization reasonably well, though software setup complexity is higher than CUDA alternatives. XMX matrix extensions provide hardware-accelerated INT8 inference for supported workloads.
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
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.
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
AJust out of reach
High-quality models that need a bit more memory
Image & Video Generation
21 of 52 models can generate images or video on your Intel Arc A750 8GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~3.5s | S |
| Stable Diffusion 1.5Image | 512×768 | ~6.9s | S |
| Realistic Vision v5.1Image | 512×768 | ~6.9s | S |
| DreamShaper 8Image | 512×768 | ~6.9s | S |
| LCM DreamShaper v7 |
Upgrade paths
See what you unlock with more powerful hardware
Upgrade options
Unlocks 33 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 52%.
~$699 MSRP
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 279%.
~$8,000 MSRP
Compare with similar
| Won’t fit |
| Llama 3.1 70B Q4 |
| — |
| Image Gen (SDXL) | Runs with sequential offload | SDXL 1.0 FP16 | ~~1m 13s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~2m 4s per image |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 | ~~2m 32s per image |
| Video Short (25f) | Won't fit | LTX Video 2B | ~~24s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~1m 11s/frame |
Gemma 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.
| 512×768 |
| ~2.1s |
| S |
| PixArt-SigmaImage | 256×256 | ~27.6s | S |
| FramePack I2VVideo | 256×256 | ~50.7s/frame | A |
| SDXL TurboImage | 256×256 | ~9.2s | A |
| SDXL LightningImage | 256×256 | ~27.5s | B |
| Stable Diffusion XL 1.0Image | 256×256 | ~1m 13s | B |
| Playground v2.5Image | 256×256 | ~41.4s | B |
| RealVisXL v5.0Image | 256×256 | ~1m 22s | B |
| DreamShaper XLImage | 256×256 | ~1m 22s | B |
| Juggernaut XL v9Image | 256×256 | ~1m 22s | B |
| Animagine XL 3.1Image | 256×256 | ~1m 22s | B |
| Pony Diffusion V6 XLImage | 256×256 | ~1m 22s | B |
| Animagine XL 4.0Image | 256×256 | ~1m 22s | B |
| Illustrious XLImage | 256×256 | ~1m 22s | B |
| Wan Video 2.1 1.3BVideo | 256×256 | ~20.2s/frame | D |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~48.3s | D |
| Flux.2 Klein 4BImage | 256×256 | ~8.3s | D |
| LTX Video 2BVideo | 256×256 | ~24s/frame | F |
| KolorsImage | 256×256 | ~55.2s | F |
| Stable CascadeImage | 256×256 | ~1m 9s | F |
| AuraFlow v0.3Image | 256×256 | ~2m 4s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~2m 32s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~27.6s | F |
| CogVideoX 2BVideo | 256×256 | ~24s/frame | F |
| HunyuanVideoVideo | 256×256 | ~50.7s/frame | F |
| ChromaImage | 256×256 | ~27.6s | F |
| Z-Image TurboImage | 256×256 | ~28.5s | F |
| Flux.1 DevImage | 256×256 | ~2m 4s | F |
| Flux.1 SchnellImage | 256×256 | ~24.2s | F |
| LTX Video 13BVideo | 256×256 | ~50.7s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~2m 18s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~12.6s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~39.6s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~34.6s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~34.6s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~13.8s | F |
| Flux.1 Fill DevImage | 256×256 | ~1m 57s | F |
| Mochi 1 PreviewVideo | 256×256 | ~45.6s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~42.4s/frame | F |
| Helios 14BVideo | 256×256 | ~52.2s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~52.2s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~52.2s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~52.2s/frame | F |
| Qwen ImageImage | 256×256 | ~46.5s | F |
| Qwen Image EditImage | 256×256 | ~46.5s | F |
| Flux.2 DevImage | 256×256 | ~21m 46s | F |
| MAGI-1Video | 256×256 | ~1m 5s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~1m 22s | 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.
8.0 GB
VRAM
$289
MSRP
$36/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 33 additional models that do not fit on the current setup.
Want more headroom? RTX 3080 10GB (10.0 GB VRAM) is the next step up.
