AIMs Catalog#
The AIM catalog includes ready-to-deploy inference microservices for popular open-weight models, from lightweight instruction-tuned models to large mixture-of-experts systems. The catalog is constantly expanded to add new models and hardware-optimized profiles. To deploy models currently not available in the catalog but available through Hugging Face and supported by vLLM, utilize the AIM base container together with a custom profile (see here).
The catalog also holds models for other tasks, such as biomolecular structure prediction, medical image segmentation and text-to-video generation. Each of these gives an API of its own and does not answer the OpenAI chat and completion APIs. The deployment guide of the AIM names its endpoints and payloads.
AIMs are available for AMD Instinct™, Radeon™, and EPYC™. Browse the catalog below to find a container for your model and hardware.
Model |
Organization |
Description |
Resources |
|---|---|---|---|
Cohere Labs |
111B parameter language model with configurable reasoning and tool use capabilities. |
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deepseek-ai/DeepSeek-R1 (stable) |
DeepSeek |
671B parameter MoE reasoning model with 37B active parameters. |
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deepseek-ai/DeepSeek-R1-0528 (stable) |
DeepSeek |
671B parameter MoE reasoning model with 37B active parameters, updated version of DeepSeek-R1. |
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deepseek-ai/DeepSeek-V3.1 (stable) |
DeepSeek |
671B parameter MoE model with 37B active parameters supporting thinking and non-thinking modes. |
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deepseek-ai/DeepSeek-V3.1-Terminus (stable) |
DeepSeek |
671B parameter MoE model with 37B active parameters, refined for language consistency and agent tasks. |
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deepseek-ai/DeepSeek-V4-Flash-0731 (stable) |
DeepSeek |
284B MoE (13B active) with 1M context, reasoning and tool use, and a fused DSpark draft for speculative decoding. |
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google/gemma-3-1b-it (stable) |
Gemma 3 1B IT is a lightweight instruction-tuned model supporting text generation with a 32K context window. |
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google/gemma-3-27b-it (stable) |
Gemma 3 27B IT is a multimodal instruction-tuned model supporting text and image input with a 128K context window. |
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google/gemma-4-12B-it (stable) |
Gemma 4 12B IT is an encoder-free multimodal instruction-tuned model with text, image, audio and video input, 256K context, from a w4a16 QAT checkpoint. |
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google/gemma-4-26B-A4B-it (stable) |
Gemma 4 26B A4B IT is a sparse mixture-of-experts, multimodal instruction-tuned model with text and image input and 256K native context. |
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google/gemma-4-31B-it (preview) |
Gemma 4 31B IT is a multimodal instruction-tuned model with text and image input, 256K native context, and Gemma 4 reasoning + tool-call parsers. |
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google/medgemma-27b-it (stable) |
Gemma 3-based 27B multimodal model fine-tuned for medical text and image tasks (X-ray, dermatology, ophthalmology, pathology, radiology reports). |
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meta-llama/Llama-3.1-405B-Instruct (stable) |
Meta |
Multilingual 405B parameter instruction-tuned language model for dialogue use cases. |
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meta-llama/Llama-3.1-8B-Instruct (stable) |
Meta |
Multilingual 8B parameter instruction-tuned language model for dialogue use cases. |
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meta-llama/Llama-3.2-1B-Instruct (stable) |
Meta |
Multilingual 1B parameter instruction-tuned language model for dialogue and on-device use cases. |
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meta-llama/Llama-3.2-3B-Instruct (stable) |
Meta |
Multilingual 3B parameter instruction-tuned language model for dialogue and on-device use cases. |
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meta-llama/Llama-3.3-70B-Instruct (stable) |
Meta |
Multilingual 70B parameter instruction-tuned language model for dialogue use cases. |
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MiniMaxAI/MiniMax-M2.5 (stable) |
MiniMax |
228B parameter mixture-of-experts language model with reasoning, tool calling, and coding capabilities. |
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MiniMaxAI/MiniMax-M3 (stable) |
MiniMax |
Native multimodal MoE model with 428B parameters, 23B active parameters, a 1M-token context window, reasoning, tool use, and coding capabilities. |
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Mistral AI |
14B parameter instruction-tuned language model with vision and function calling capabilities. |
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Mistral AI |
14B parameter instruction-tuned language model with vision and function calling capabilities. |
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Mistral AI |
675B parameter granular MoE multimodal model with 41B active parameters and vision capabilities. |
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Mistral AI |
24B parameter instruction-tuned language model (Mistral Small 3) with native function calling. Text-only. |
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Mistral AI |
24B parameter instruction-tuned language model with vision and function calling capabilities. |
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Mistral AI |
Sparse MoE language model with 141B total parameters across 8 experts and function calling support. |
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mistralai/Mixtral-8x7B-Instruct-v0.1 (stable) |
Mistral AI |
Sparse MoE language model with 47B total parameters across 8 experts. |
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moonshotai/Kimi-K3 (release candidate) |
Moonshot AI |
2.8T-parameter native multimodal MoE with Kimi Delta Attention, a 1M-token context window, and MXFP4 weights. |
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openai/gpt-oss-120b (release candidate) |
OpenAI |
Open-weight 117B parameter MoE model with 5.1B active parameters and configurable reasoning. |
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openai/gpt-oss-20b (stable) |
OpenAI |
Open-weight 21B parameter MoE model with 3.6B active parameters for lower-latency use cases. |
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Qwen/Qwen3-235B-A22B (stable) |
Qwen |
235B parameter MoE language model with 22B active parameters and dual thinking modes. |
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Qwen/Qwen3-32B (stable) |
Qwen |
32.8B parameter dense language model with dual thinking modes and multilingual support. |
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Qwen/Qwen3-Coder-Next (stable) |
Qwen |
80B parameter MoE coding agent model with 3B active parameters and hybrid attention architecture. |
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Qwen/Qwen3-VL-235B-A22B-Instruct (stable) |
Qwen |
236B parameter MoE vision-language model with 22B active parameters and multimodal capabilities. |
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Qwen/Qwen3-VL-235B-A22B-Thinking (stable) |
Qwen |
236B parameter MoE vision-language model with reasoning-enhanced thinking capabilities. |
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Qwen/Qwen3.8-27B (stable) |
Qwen |
27B parameter dense vision-language model with hybrid Gated DeltaNet attention and flexible thinking control. |
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Wan-AI/Wan2.2-T2V-A14B-Diffusers (stable) |
Wan-AI |
Wan2.2 T2V-A14B text-to-video diffusion model (Diffusers format), served via vLLM-Omni. |
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zai-org/GLM-4.7 (stable) |
Z.ai |
GLM-4.7 is a large language model with multi-turn conversation, tool use, and reasoning capabilities. |
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zai-org/GLM-5.2 (stable) |
Z.ai |
GLM-5.2 is a large Mixture-of-Experts language model with multi-turn conversation, tool use, and reasoning capabilities. |
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biohub/esmc-300m (preview) |
biohub |
Biohub ESMC 300M protein language model serving per-sequence embeddings. |
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bioptimus/h-optimus-0 (preview) |
bioptimus |
Bioptimus H-optimus-0 pathology foundation model (ViT, 1536-dim embeddings). |
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cytokineking/freebindcraft (preview) |
cytokineking |
FreeBindCraft protein binder design (no-PyRosetta mode) on AMD Instinct GPUs. |
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dauparas/proteinmpnn (preview) |
dauparas |
Protein sequence design model. |
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facebookresearch/dinov2-vitl14-embedding (preview) |
facebookresearch |
Served DINOv2 ViT-L/14 image embedding AIM. |
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gromacs/gromacs (preview) |
gromacs |
GROMACS molecular dynamics simulation via ADR-0008 long-running job container. |
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meta-models/Muse-Glimmer-30B (stable) |
meta-models |
30 billion parameter dense vision-language model for local agentic work, with sliding-window attention on 39 of 52 layers and a 128K context window. |
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mit/boltz2 (preview) |
mit |
Biomolecular structure prediction model. |
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molecularai/reinvent4 (preview) |
molecularai |
Molecular drug design tool running on AMD Instinct GPUs. |
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monai/spleen_deepedit_annotation (stable) |
monai |
Spleen DeepEdit interactive 3D CT segmentation. |
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monai/swinunetr (stable) |
monai |
Multi-organ abdominal CT segmentation model. |
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monai/vista3d (stable) |
monai |
3D promptable CT segmentation foundation model: automatic 117-class, class-prompt, or point-prompt modes. |
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monai/wholeBody_ct_segmentation (preview) |
monai |
104-class whole-body CT segmentation. |
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monai |
133-class whole-brain T1w MRI parcellation. |
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openfold/openfold3 (stable) |
openfold |
Biomolecular structure prediction model. |
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rssrwn/semlaflow (preview) |
rssrwn |
Small-molecule generation model using equivariant flow matching (SMILES and 3D mol-block output), served as a custom AIM image. |
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westlake-repl/saprot-650m-af2 (preview) |
westlake-repl |
Westlake University SaProt 650M AF2 structure-aware protein language model serving per-sequence embeddings. |
Model |
Organization |
Description |
Resources |
|---|---|---|---|
deepseek-ai/DeepSeek-R1-Distill-Qwen-14B (preview) |
DeepSeek |
14B parameter distilled reasoning model based on Qwen-2.5, optimized for CPU inference on AMD EPYC. |
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DeepSeek |
7B parameter distilled reasoning model based on Qwen-2.5, optimized for CPU inference on AMD EPYC. |
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google/gemma-3-12b-it (stable) |
Gemma 3 12B Instruct multimodal model from Google DeepMind. |
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google/gemma-3-1b-it (stable) |
Gemma 3 1B Instruct multimodal model from Google DeepMind. |
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google/gemma-3-4b-it (stable) |
Gemma 3 4B Instruct multimodal model from Google DeepMind. |
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google/gemma-4-E4B-it (stable) |
Gemma 4 E4B is Google’s efficient instruction-tuned model with a ~4B effective-parameter MatFormer architecture for low-latency on-device and CPU inference. |
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meta-llama/Llama-3.1-8B-Instruct (stable) |
Meta |
Multilingual 8B parameter instruction-tuned language model for dialogue use cases. |
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meta-llama/Llama-3.2-1B-Instruct (stable) |
Meta |
Multilingual 1B parameter instruction-tuned language model for dialogue and on-device use cases. |
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meta-llama/Llama-3.2-3B-Instruct (stable) |
Meta |
Multilingual 3B parameter instruction-tuned language model for dialogue and on-device use cases. |
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Qwen/Qwen2.5-Coder-7B-Instruct (stable) |
Qwen |
7B parameter coding and instruction-following model optimized for CPU inference on AMD EPYC. |
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Qwen/Qwen2.5-VL-7B-Instruct (preview) |
Qwen |
7B parameter vision-language model optimized for CPU inference on AMD EPYC. |
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Qwen/Qwen3-0.6B (stable) |
Qwen |
Reasoning-enhanced 0.6B parameter LLM with thinking/non-thinking mode switching, excelling in math, coding, and multi-turn conversations. |
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Qwen/Qwen3-1.7B (stable) |
Qwen |
Reasoning-enhanced 1.7B parameter LLM with thinking/non-thinking mode switching, excelling in math, coding, and multi-turn conversations. |
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Qwen/Qwen3-30B-A3B (stable) |
Qwen |
Mixture-of-experts 30B (3B active) LLM with thinking/non-thinking mode switching, advanced reasoning, agent capabilities, and 100+ language support. |
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Qwen/Qwen3-4B (stable) |
Qwen |
Reasoning-enhanced 4B parameter LLM with thinking/non-thinking mode switching, excelling in math, coding, and multi-turn conversations. |
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Qwen/Qwen3-8B (stable) |
Qwen |
Reasoning-enhanced 8B parameter LLM with thinking/non-thinking mode switching, excelling in math, coding, and multi-turn conversations. |
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Qwen/Qwen3.5-4B (stable) |
Qwen |
Qwen3.5-4B is a 4B parameter LLM with thinking/non-thinking dual-mode reasoning, strong math and coding ability, and multilingual support. |
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Qwen/Qwen3.5-9B (stable) |
Qwen |
Qwen3.5-9B is a 9B parameter LLM with thinking/non-thinking dual-mode reasoning, strong math and coding ability, and multilingual support. |
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Qwen/Qwen3.6-35B-A3B (stable) |
Qwen |
Qwen3.6-35B-A3B is a Mixture-of-Experts LLM with 35B total parameters and ~3B active per token, balancing high quality with efficient inference. |
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ibm-granite/granite-3.3-2b-instruct (stable) |
ibm-granite |
2B parameter instruction-tuned language model with 128K context length and improved reasoning capabilities. |
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ibm-granite/granite-4.1-3b (stable) |
ibm-granite |
3B parameter long-context instruct model with 131K context length and enhanced capabilities for summarization, RAG, and code tasks. |
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ibm-granite/granite-4.1-8b (stable) |
ibm-granite |
8B parameter long-context instruct model with 131K context length and enhanced capabilities for summarization, RAG, and code tasks. |
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microsoft/Phi-3.5-mini-instruct (stable) |
microsoft |
3.8B parameter language model optimized for reasoning, math, and code tasks in memory-constrained environments. |
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unsloth/gpt-oss-20b-BF16 (stable) |
unsloth |
gpt-oss-20B is OpenAI’s open-weight 20B Mixture-of-Experts model (BF16 conversion by Unsloth) with strong reasoning and tool-use capabilities. |
Model |
Organization |
Description |
Resources |
|---|---|---|---|
google/gemma-3n-E4B-it (preview) |
Gemma 3n E4B IT is a gated multimodal instruction-tuned model supporting text, image, video, and audio inputs. |
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meta-llama/Llama-3.1-8B-Instruct (stable) |
Meta |
Multilingual 8B parameter instruction-tuned language model for dialogue use cases. |
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openai/whisper-large-v3 (stable) |
OpenAI |
Whisper Large v3 is a multilingual speech recognition and translation model from OpenAI. |
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Qwen/Qwen3-VL-8B-Instruct (stable) |
Qwen |
8B parameter vision-language model with advanced multimodal reasoning. |
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Qwen/Qwen3.5-9B (stable) |
Qwen |
9B parameter hybrid language model with Gated DeltaNet and dual thinking modes. |
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Qwen/Qwen3.6-35B-A3B (stable) |
Qwen |
Qwen3.6-35B-A3B is a Mixture-of-Experts LLM with 35B total parameters and ~3B active per token, balancing high quality with efficient inference. |
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zai-org/GLM-4.7-Flash (preview) |
Z.ai |
30B-A3B MoE language model with balanced performance and efficiency for lightweight deployment. |