AMD
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 AMD Instinct MI250X 128GB is the higher-tier CDNA 2 OAM accelerator, featuring the same 128 GB HBM2e as the MI250 but with higher compute throughput (383 vs 362 TFLOPS FP16). It was AMD's primary competitor to the NVIDIA A100 80GB and was widely deployed in HPC clusters. Full ROCm support makes it a production-ready platform for LLM inference, though it has been superseded by the MI300X for new deployments.
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
CDNA 2 powers the Instinct MI210 and MI250/MI250X accelerators. It introduced multi-die packaging with up to 128 GB HBM2e and Infinity Fabric for die-to-die communication.
AI Relevance
With up to 128 GB HBM2e memory and strong ROCm support, CDNA 2 GPUs can host large language models. The MI250X was used in the Frontier exascale supercomputer and supports major AI frameworks.
Conselho de compra
Excelente escolha para IA local
Roda 35 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
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best upgrade itinerary
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
SQwen 3.5 122B A10B 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, lm-studio.
Coding
SQwen3-Coder-Next is a specialized fit for 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.
Agentic Coding
SDevstral 2 123B Instruct 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, 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
SQwen 3.5 122B A10B matches RAG 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.
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
Gemma 4 31B
Phi-4-reasoning-plus 14BNemotron Cascade 2 30B A3BQwen 3 8BGPT-OSS 20BQwen 3.5 4B
EXAONE 4.0 32BMinistral 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-5.2GLM-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
56 of 56 models can generate images or video on your AMD Instinct MI250X 128GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 100ms | S |
| Stable Diffusion 1.5Image | 512×768 | 200ms | S |
| Realistic Vision v5.1Image | 512×768 | 200ms | S |
| DreamShaper 8Image | 512×768 | 200ms | S |
| LCM DreamShaper v7Image | 512×768 | 100ms | S |
| PixArt-SigmaImage | 1024×1024 | 800ms | S |
| FramePack I2VVideo | 1280×720 | ~1.5s/frame | S |
| SDXL TurboImage | 512×512 | 100ms | S |
| SDXL LightningImage | 1024×1024 | 300ms | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | 800ms | S |
| Playground v2.5Image | 1024×1024 | ~1.3s | S |
| RealVisXL v5.0Image | 1024×1024 | 900ms | S |
| DreamShaper XLImage | 1024×1024 | 900ms | S |
| Juggernaut XL v9Image | 1024×1024 | 900ms | S |
| Animagine XL 3.1Image | 1024×1024 | 900ms | S |
| Pony Diffusion V6 XLImage | 1024×1024 | 900ms | S |
| Animagine XL 4.0Image | 1024×1024 | 900ms | S |
| Illustrious XLImage | 1024×1024 | 900ms | S |
| Wan Video 2.1 1.3BVideo | 480×832 | 600ms/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~1.5s | S |
| Flux.2 Klein 4BImage | 1024×1024 | 300ms | S |
| LTX Video 2BVideo | 1280×720 | 700ms/frame | S |
| KolorsImage | 1024×1024 | ~1.7s | S |
| Stable CascadeImage | 1024×1024 | ~2.1s | S |
| AuraFlow v0.3Image | 1536×1536 | ~3.8s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~4.6s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | 800ms | S |
| CogVideoX 2BVideo | 720×480 | 700ms/frame | S |
| HunyuanVideoVideo | 720×1280 | ~1.5s/frame | S |
| ChromaImage | 1024×1024 | 800ms | S |
| Z-Image TurboImage | 1536×1536 | 900ms | S |
| Flux.1 DevImage | 1024×1024 | ~3.8s | S |
| Flux.1 SchnellImage | 1024×1024 | 700ms | S |
| LTX Video 13BVideo | 1280×720 | ~1.5s/frame | S |
| Flux.1 Kontext DevImage | 1024×1024 | ~4.2s | S |
| AnimateDiff v1.5.3Video | 512×768 | 400ms/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | ~1.2s/frame | S |
| CogVideoX 5BVideo | 720×480 | ~1s/frame | S |
| Wan2.2 TI2V 5BVideo | 832×480 | ~1s/frame | S |
| Flux.2 Klein 9BImage | 1024×1024 | 400ms | S |
| Flux.1 Fill DevImage | 1024×1024 | ~3.5s | S |
| Krea 2Image | 1024×1024 | ~1.1s | S |
| Sulphur 2Video | 1280×720 | ~1.3s/frame | S |
| Ideogram 4Image | 2048×2048 | ~1s | S |
| Mochi 1 PreviewVideo | 848×480 | ~1.4s/frame | S |
| HunyuanVideo 1.5Video | 720×1280 | ~1.3s/frame | S |
| Helios 14BVideo | 1280×720 | ~1.6s/frame | S |
| SkyReels V2 14BVideo | 1280×720 | ~1.6s/frame | S |
| Wan Video 2.1 14BVideo | 720×1280 | ~1.6s/frame | S |
| Wan Video 2.2 14BVideo | 720×1280 | ~1.6s/frame | S |
| Qwen ImageImage | 1024×1024 | ~1.4s | S |
| Qwen Image EditImage | 1024×1024 | ~1.4s | S |
| LTX-2 22BVideo | 1920×1088 | ~1.9s/frame | S |
| Flux.2 DevImage | 1024×1024 | ~39.5s | S |
| MAGI-1Video | 1280×720 | ~2s/frame | S |
| HunyuanImage 3.0Image | 256×256 | ~2.5s | 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. Infinity Fabric provides 800 GB/s inter-GPU bandwidth with 15% overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× AMD | 128 GB | 356/380 | 3,200 GB/s |
| 2× AMD | 256 GB | 368/380 | 5,440 GB/s |
| 4× AMD | 512 GB | 377/380 | 10,880 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.85× per additional GPU.
Upgrade paths
See what you unlock with more powerful hardware
Opções de upgrade
Unlocks 21 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 69%.
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 24%.
~$30,000 MSRP
NVIDIA B200 180GBMaior saltoUnlocks 8 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 54%.
~$30,000 MSRP
Unlocks 12 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 26%.
~$20,000 MSRP
Unlocks 13 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 44%.
~$8,000 MSRP
AMD Instinct MI250X 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: 97/100). See the full compatibility list above.
AMD Instinct MI250X 128GB has 128 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, AMD Instinct MI250X 128GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on AMD Instinct MI250X 128GB, we recommend Qwen3-Coder-Next. It achieves 105.7 tokens per second with 256K context window. Qwen3-Coder-Next is a specialized fit for 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.
There are 5 upgrade path(s) from AMD Instinct MI250X 128GB: AMD Instinct MI250X 128GB, NVIDIA H200 141GB. Upgrading would unlock larger models and faster inference speeds.
Yes, AMD Instinct MI250X 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.
AMD Instinct MI250X 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.
AMD Instinct MI250X 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, AMD Instinct MI250X 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 AMD Instinct MI250X 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.
AMD Instinct MI250X 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.
AMD Instinct MI250X 128GB supports up to 4× GPU scaling via Infinity Fabric at 800 GB/s. With 4× GPUs, you get 512 GB effective memory with a 0.85× 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.
Infinity Fabric is recommended for AMD Instinct MI250X 128GB multi-GPU inference, providing 800 GB/s interconnect bandwidth with only 15% scaling overhead. PCIe-only setups work but have higher overhead (~25%) due to limited inter-GPU bandwidth.
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