Intel
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Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
A Intel Data Center GPU Max 1550 (Ponte Vecchio) é a GPU de data center principal da Intel, com 128 GB de memória HBM2e e 3,2 TB/s de largura de banda em um design multi-tile massivo. Compete diretamente com a NVIDIA A100 para treinamento e inferência de IA em grande escala. Construída na arquitetura Xe HPC com oneAPI e SYCL, a grande capacidade de VRAM permite inferência de modelos de 70B+ em FP16 em uma única placa.
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) | Runs natively | Qwen 3 30B Q4 |
| LLM Large (70B) | Runs natively | Llama 3.1 70B Q4 |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 |
| Video Short (25f) | Runs natively | LTX Video 2B |
| Video Long (100f) | Runs natively | Wan Video 14B |
Architecture
Ponte Vecchio is Intel's datacenter GPU architecture powering the Max series accelerators. It uses advanced multi-tile packaging combining Intel 7 and TSMC N5 processes, with up to 128 GB HBM2e memory.
AI Relevance
With 128 GB HBM2e and oneAPI support, the Max 1550 can host large AI models. Used in the Aurora exascale supercomputer. However, the AI software ecosystem is smaller than CUDA or ROCm.
Conselho de compra
Excelente escolha para IA local
Roda 36 de 50 modelos principais bem — um ótimo coringa para inferência local.
128.0 GB
VRAM
$15,000
Preço sugerido
$117/GB
Custo por GB de VRAM
Melhores modelos para esta 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 2 additional models that do not fit on the current setup.
Quer mais margem? NVIDIA H200 141GB (141.0 GB VRAM) é o próximo passo.
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, 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
SThis 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, lm-studio.
Reasoning
SDevstral 2 123B Instruct 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, lm-studio.
RAG
SThis model is a direct match for rag. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, lm-studio.
Qwen 3.5 122B A10B
Mistral Small 4 119BDevstral 2 123B Instruct
GPT-OSS 120B
Command A 111BPixtral Large 124BLeanstral 119B A6BQwen3-Coder-NextQwen 2.5 VL 72BQwen 3.6 35B A3BQwen3-Coder 30B A3B InstructQwen 3.5 27BQwen3-VL 30B A3B InstructQwen 3.5 35B A3BQwen 3.6 27BQwen 3 32BMagistral Small 2507Devstral Small 2 24B InstructQwen 3 30B A3B
Nemotron 3 Nano 30BQwen 3.5 9BQwen 3 14BDevstral Small 1.1
Phi-4-reasoning-plus 14B
Gemma 4 31BNemotron Cascade 2 30B A3BQwen 3 8BGPT-OSS 20BQwen 3.5 4B
EXAONE 4.0 32BGemma 4 26B A4BMinistral 3 14BNemotron Nano 8BPhi-4 Mini Reasoning 4B
Jina Embeddings v3
BGE M3Qwen 3.5 397B A17B
Kimi K2.5Kimi K2.6
DeepSeek V4 ProDeepSeek V4 Flash
GLM-5.1GLM-5DeepSeek V3.2Qwen 3 235B A22BQwen3-Coder 480B A35B InstructDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671BQuase ao alcance
Modelos de alta qualidade que precisam de um pouco mais de memória
Image & Video Generation
52 of 52 models can generate images or video on your Intel Data Center GPU Max 1550 128GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 500ms | S |
| Stable Diffusion 1.5Image | 512×768 | ~1s | S |
| Realistic Vision v5.1Image | 512×768 | ~1s | S |
| DreamShaper 8Image | 512×768 | ~1s | S |
| LCM DreamShaper v7Image | 512×768 | 300ms | S |
| PixArt-SigmaImage | 1024×1024 | ~3.8s | S |
| FramePack I2VVideo | 1280×720 | ~7s/frame | S |
| SDXL TurboImage | 512×512 | 500ms | S |
| SDXL LightningImage | 1024×1024 | ~1.4s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~3.8s | S |
| Playground v2.5Image | 1024×1024 | ~5.7s | S |
| RealVisXL v5.0Image | 1024×1024 | ~4.3s | S |
| DreamShaper XLImage | 1024×1024 | ~4.3s | S |
| Juggernaut XL v9Image | 1024×1024 | ~4.3s | S |
| Animagine XL 3.1Image | 1024×1024 | ~4.3s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~4.3s | S |
| Animagine XL 4.0Image | 1024×1024 | ~4.3s | S |
| Illustrious XLImage | 1024×1024 | ~4.3s | S |
| Wan Video 2.1 1.3BVideo | 480×832 | ~2.8s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~6.7s | S |
| Flux.2 Klein 4BImage | 1024×1024 | ~1.1s | S |
| LTX Video 2BVideo | 1280×720 | ~3.3s/frame | S |
| KolorsImage | 1024×1024 | ~7.6s | S |
| Stable CascadeImage | 1024×1024 | ~9.5s | S |
| AuraFlow v0.3Image | 1536×1536 | ~17.1s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~20.9s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~3.8s | S |
| CogVideoX 2BVideo | 720×480 | ~3.3s/frame | S |
| HunyuanVideoVideo | 720×1280 | ~7s/frame | S |
| ChromaImage | 1024×1024 | ~3.8s | S |
| Z-Image TurboImage | 1536×1536 | ~3.9s | S |
| Flux.1 DevImage | 1024×1024 | ~17.1s | S |
| Flux.1 SchnellImage | 1024×1024 | ~3.3s | S |
| LTX Video 13BVideo | 1280×720 | ~7s/frame | S |
| Flux.1 Kontext DevImage | 1024×1024 | ~19s | S |
| AnimateDiff v1.5.3Video | 512×768 | ~1.7s/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | ~5.5s/frame | S |
| CogVideoX 5BVideo | 720×480 | ~4.8s/frame | S |
| Wan2.2 TI2V 5BVideo | 832×480 | ~4.8s/frame | S |
| Flux.2 Klein 9BImage | 1024×1024 | ~1.9s | S |
| Flux.1 Fill DevImage | 1024×1024 | ~16.2s | S |
| Mochi 1 PreviewVideo | 848×480 | ~6.3s/frame | S |
| HunyuanVideo 1.5Video | 720×1280 | ~5.8s/frame | S |
| Helios 14BVideo | 1280×720 | ~7.2s/frame | S |
| SkyReels V2 14BVideo | 1280×720 | ~7.2s/frame | S |
| Wan Video 2.1 14BVideo | 720×1280 | ~7.2s/frame | S |
| Wan Video 2.2 14BVideo | 720×1280 | ~7.2s/frame | S |
| Qwen ImageImage | 1024×1024 | ~6.4s | S |
| Qwen Image EditImage | 1024×1024 | ~6.4s | S |
| Flux.2 DevImage | 1024×1024 | ~3m 0s | S |
| MAGI-1Video | 1280×720 | ~8.9s/frame | S |
| HunyuanImage 3.0Image | 256×256 | ~11.3s | D |
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.
Multi-GPU scaling
Scale out with multiple GPUs for larger models. PCIe interconnect with 20% scaling overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× Intel | 128 GB | 351/374 | 3,200 GB/s |
| 2× Intel | 256 GB | 363/374 | 5,120 GB/s |
| 4× Intel | 512 GB | 371/374 | 10,240 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.8× per additional GPU.
Upgrade paths
See what you unlock with more powerful hardware
Opções de upgrade
4× Intel Data Center GPU Max 1550 128GBMulti-GPUUnlocks 20 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 55%.
Infinity Fabric gives this scale-out path a cleaner inter-GPU story than PCIe-only builds.
~$15,000 MSRP
NVIDIA H200 141GBPróximo passoUnlocks 2 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 32%.
~$30,000 MSRP
Unlocks 12 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 34%.
~$20,000 MSRP
Unlocks 13 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 51%.
~$8,000 MSRP
Intel Data Center GPU Max 1550 128GB (128 GB VRAM) can run these top models: Qwen 3.5 122B A10B (score: 99/100), Mistral Small 4 119B (score: 97/100), Devstral 2 123B Instruct (score: 96/100). See the full compatibility list above.
Intel Data Center GPU Max 1550 128GB has 128 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, Intel Data Center GPU Max 1550 128GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on Intel Data Center GPU Max 1550 128GB, we recommend Qwen3-Coder-Next. It achieves 136.1 tokens per second with 256K 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 Data Center GPU Max 1550 128GB: Intel Data Center GPU Max 1550 128GB, NVIDIA H200 141GB. Upgrading would unlock larger models and faster inference speeds.
Yes, Intel Data Center GPU Max 1550 128GB with 128 GB of usable memory can run Flux.1 Dev at FP16 natively. Flux is a 12B parameter diffusion transformer that produces high-quality images. You can also run the Schnell variant for faster generation.
Intel Data Center GPU Max 1550 128GB (128 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. Flux.1 Dev also runs natively for state-of-the-art image quality. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.
Intel Data Center GPU Max 1550 128GB is excellent for AI image generation. With 128 GB of usable memory, it runs all major diffusion models including Flux.1, SDXL, and Stable Diffusion 3.5 at full precision. You can generate high-resolution images quickly and even handle video generation models.
Yes, Intel Data Center GPU Max 1550 128GB with 128 GB of usable memory can run Qwen 3.5 27B at Q8 (near-lossless, ~28.9 GB) or even FP16 (~55.4 GB) depending on your context needs. This setup provides an excellent experience with this model. Use Ollama or vLLM for best results.
With 128 GB VRAM on Intel Data Center GPU Max 1550 128GB, use Q8_0 for most models — it is near-lossless and you have the memory for it. For 70B+ models, Q6_K offers excellent quality. Reserve Q4_K_M for 100B+ models or when you need maximum context length.
Intel Data Center GPU Max 1550 128GB already has strong memory bandwidth, so the next limit is often memory capacity and context headroom rather than raw decode speed. For local LLMs, fit first and bandwidth second is the right mental model.
Intel Data Center GPU Max 1550 128GB 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 Data Center GPU Max 1550 128GB can still be useful.
Intel Data Center GPU Max 1550 128GB supports up to 4× GPU scaling via Infinity Fabric. With 4× GPUs, you get 512 GB effective memory with a 0.8× scaling factor per GPU. This enables running models like Qwen 3.5 397B A17B and Kimi K2.5 that don't fit on a single card.
Intel Data Center GPU Max 1550 128GB uses PCIe for multi-GPU communication, which has approximately 20% scaling overhead. For best multi-GPU performance, consider NVLink-equipped variants.
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