# Janus-Pro-1B by DeepSeek-community: Open-Weight Model Source: https://savrn.com/models/janus-pro-1b Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve Janus-Pro-1B (2.1B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 4.2 GB | 5.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 2.1 GB | 2.5 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 1.0 GB | 1.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 23, 2026. [Janus-Pro-1B on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/janus-pro-1b/gpus) ## Model Card By DeepSeek-community, published under mit, revision 1655280bb759. ### 1. Introduction Janus-Pro is a novel autoregressive framework that unifies multimodal understanding and generation. It addresses the limitations of previous approaches by decoupling visual encoding into separate pathways, while still utilizing a single, unified transformer architecture for processing. The decoupling not only alleviates the conflict between the visual encoder’s roles in understanding and generation, but also enhances the framework’s flexibility. Janus-Pro surpasses previous unified model and matches or exceeds the performance of task-specific models. The simplicity, high flexibility, and effectiveness of Janus-Pro make it a strong candidate for next-generation unified multimodal models. [Github Repository](https://github.com/deepseek-ai/Janus) #### 2. Model Summary Janus-Pro is a unified understanding and generation MLLM, which decouples visual encoding for multimodal understanding and generation. Janus-Pro is constructed based on the DeepSeek-LLM-1.5b-base/DeepSeek-LLM-7b-base. For multimodal understanding, it uses the [SigLIP-L](https://huggingface.co/timm/ViT-L-16-SigLIP-384) as the vision encoder, which supports 384 x 384 image input. For image generation, Janus-Pro uses the tokenizer from [here](https://github.com/FoundationVision/LlamaGen) with a downsample rate of 16. ### 3. Usage Examples #### Single Image Inference [Read the full model card (460 words)](https://savrn.com/models/janus-pro-1b/card) ## Configuration Architecture JanusForConditionalGeneration Context length (tokens) 16,384 Layers 24 Hidden size 2,048 Feed-forward size 5,632 Attention heads 16 Key/value heads 16 Head dimension 128 Vocabulary size 102,400 RoPE base 10000 Stored precision bfloat16 Model type janus ## Identity and Version Repository deepseek-community/Janus-Pro-1B Publisher DeepSeek-community Task Any to any Modality Multimodal Library transformers Parameters 2.1B parameters Languages Not stated by the source Revision 1655280bb75959cc1cb85529a2a8b26e7016072e First published 2025-03-01 Last updated 2026-03-18 ## Files and Weights 13 files, 4.2 GB in total. The weights are 1 file totalling 4.2 GB in safetensors. Weights1 file · 4.2 GB Configuration5 files · 3.5 KB Tokenizer2 files · 7.7 MB Documentation1 file · 5.0 KB Other3 files · 629.1 KB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 4.2 GB | 9d1a416f95fb | | config.json | Configuration | 1.9 KB | — | | generation_config.json | Configuration | 233 B | — | | preprocessor_config.json | Configuration | 465 B | — | | processor_config.json | Configuration | 79 B | — | | special_tokens_map.json | Configuration | 792 B | — | | README.md | Documentation | 5.0 KB | — | | chat_template.jinja | Other | 1.0 KB | — | | janus_pro_teaser1.png | Other | 98.0 KB | — | | janus_pro_teaser2.png | Other | 530.0 KB | dcd9be432b9d | | .gitattributes | Repository | 1.6 KB | — | | tokenizer.json | Tokenizer | 7.6 MB | — | | tokenizer_config.json | Tokenizer | 105.9 KB | — | ## License and Download License mit Access Open weights, no gate Download size 4.2 GB [Download from DeepSeek-community](https://huggingface.co/deepseek-community/Janus-Pro-1B) Released by DeepSeek-community through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Described by arXiv:2501.17811 ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 4.2 GB | | 16-bit | 4.2 GB | | 8-bit | 2.1 GB | | 4-bit | 1.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About Janus-Pro-1B ### How much GPU memory does Janus-Pro-1B need? About 5 GB at 16-bit and 1.2 GB at 4-bit: the weights (2.1B parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run Janus-Pro-1B on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use Janus-Pro-1B commercially? Yes. Janus-Pro-1B is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ### What is Janus-Pro-1B's context length? 16,384 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Any to any ### [gemma-4-31B-it-assistant](https://savrn.com/models/gemma-4-31b-it-assistant) [Google](https://savrn.com/model-publishers/google) Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. 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Their diverse sizes make them deployable in environments ranging from… Open weights apache-2.0 transformers [View model](https://savrn.com/models/gemma-4-12b-it-qat-q4-0-gguf) ## DeepSeek-community [All models and datasets](https://savrn.com/model-publishers/deepseek-community) ## Versions - [1655280bb759](https://savrn.com/models/janus-pro-1b/versions/1655280bb759) · current 2026-09-18 ## Explore More - [All any to any models](https://savrn.com/models/tasks/any-to-any) - [All models under mit](https://savrn.com/models/licenses/mit) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-21. - [Hugging Face record](https://huggingface.co/deepseek-community/Janus-Pro-1B) - [How the hub is built](https://savrn.com/model-hub/methodology)