# vllm-translategemma-4b-it by Infomaniak Network SA Source: https://savrn.com/models/vllm-translategemma-4b-it Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve vllm-translategemma-4b-it (5B 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 | 9.9 GB | 11.9 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 | 5.0 GB | 6.0 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 | | 4-bit | 2.5 GB | 3.0 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 | 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 25, 2026. [vllm-translategemma-4b-it on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/vllm-translategemma-4b-it/gpus) ## SAVRN's Notes on vllm-translategemma-4b-it Infomaniak Network SA published this, not Google. It is google/translategemma-4b-it with the chat template rewritten so vLLM can take the source and target language codes inline in the message, marked by double arrows, rather than in separate fields. Image and text in, text out, 5 billion parameters, a 131,072-token window with a 1,024-token sliding window. The 16-bit weights are 9.9 GB and need 11.9 GB; 8-bit needs 6.0 GB and 4-bit needs 3.0 GB. The cheapest fit we list is one MI300X with 192 GB at $1.85 an hour. The Gemma Terms of Use govern it whoever re-hosts the weights: commercial use is allowed provided the Prohibited Use Policy travels with every copy you pass on, so redistribution carries paperwork that an internal deployment does not. Confirm your serving stack honors the modified template, and know that our file holds no evaluations for it and no host prices. SAVRN Research, 2026-09-18 ## Model Card This is a modified version of google/translategemma-4b-it optimized for deployment with vLLM. The original TranslateGemma model requires a structured payload with dedicated sourcelangcode and targetlangcode fields: However, vLLM does not support these custom content parameters. To maintain compatibility, the chat template has been modified to encode language codes directly in the message content using a delimiter-based format: Format: >>{sourcelang} >>{targetlang} >>{texttotranslate} If you need to provide a custom prompt input The original model uses the new Transformers RoPE configuration format with separate attention type settings: This has been simplified for vLLM compatibility: The… Excerpt from the card by Infomaniak Network SA, licensed gemma. ## Configuration Architecture Gemma3ForConditionalGeneration Context length (tokens) 131,072 Layers 34 Hidden size 2,560 Feed-forward size 10,240 Attention heads 8 Key/value heads 4 Head dimension 256 Vocabulary size 262,208 Sliding window (tokens) 1,024 RoPE base 1,000,000 Model type gemma3 ## Identity and Version Repository Infomaniak-AI/vllm-translategemma-4b-it Publisher Infomaniak Network SA Task Image and text to text Modality Image and text Library transformers Parameters 5B parameters Languages Not stated by the source Revision cb3e0b2504f0db832982cca7fafd3ea4aaf777a0 First published 2026-01-26 Last updated 2026-01-27 ## Files and Weights 16 files, 8.6 GB in total. The weights are 2 files totalling 8.6 GB in safetensors. Weights2 files · 8.6 GB Configuration8 files · 104.8 KB Tokenizer3 files · 39.2 MB Documentation1 file · 19.3 KB Other1 file · 17.3 KB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model-00001-of-00002.safetensors | Weights | 5.0 GB | 6187566ec8d0 | | model-00002-of-00002.safetensors | Weights | 3.6 GB | 40c6a896b227 | | added_tokens.json | Configuration | 35 B | — | | config.json | Configuration | 2.5 KB | — | | generation_config.json | Configuration | 229 B | — | | model.safetensors.index.json | Configuration | 90.6 KB | — | | preprocessor_config.json | Configuration | 570 B | — | | processor_config.json | Configuration | 70 B | — | | special_tokens_map.json | Configuration | 662 B | — | | test_chat_template.py | Configuration | 10.2 KB | — | | README.md | Documentation | 19.3 KB | — | | chat_template.jinja | Other | 17.3 KB | — | | .gitattributes | Repository | 1.6 KB | — | | tokenizer.json | Tokenizer | 33.4 MB | 7d4046bf0505 | | tokenizer.model | Tokenizer | 4.7 MB | 1299c11d7cf6 | | tokenizer_config.json | Tokenizer | 1.2 MB | — | ## License and Download License gemma Access Open weights, no gate Download size 8.6 GB [Download from Infomaniak Network SA](https://huggingface.co/Infomaniak-AI/vllm-translategemma-4b-it) Released by Infomaniak Network SA through its official repository on Hugging Face. ## Built From - Described by arXiv:2503.19786 - Described by arXiv:2601.09012 ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 8.6 GB | | 16-bit | 9.9 GB | | 8-bit | 5.0 GB | | 4-bit | 2.5 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare vllm-translategemma-4b-it - [chandra-ocr-2 vs vllm-translategemma-4b-it](https://savrn.com/models/compare/chandra-ocr-2-vs-vllm-translategemma-4b-it) - [Qwen3.5-4B vs vllm-translategemma-4b-it](https://savrn.com/models/compare/qwen3-5-4b-vs-vllm-translategemma-4b-it) ## Questions About vllm-translategemma-4b-it ### How much GPU memory does vllm-translategemma-4b-it need? About 11.9 GB at 16-bit and 3 GB at 4-bit: the weights (5B parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run vllm-translategemma-4b-it 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 vllm-translategemma-4b-it commercially? Yes, with conditions. vllm-translategemma-4b-it is released under Gemma Terms of Use. Gemma models are released under Google's Gemma Terms of Use, which permit commercial use and redistribution subject to the Gemma Prohibited Use Policy, whose restrictions must be passed on to anyone the model is distributed to. ### What is vllm-translategemma-4b-it's context length? 131,072 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Image and text to text ### [Gemma4-E2B-it-W4A16](https://savrn.com/models/gemma4-e2b-it-w4a16) [88plug AI Lab](https://savrn.com/model-publishers/88plug) These weights are compressed-tensors (pack-quantized / int-quantized). Do not deploy via TGI “text-generation-inference” paths — that backend does not load our CT format and produces opaque worker/load errors. INT4 post-training quantization of google/gemma-4-e2b-it — Google's 2B-active multimodal MoE with 128 experts. The smallest capable multimodal MoE. Runs on any 8 GB GPU. Quantized with datafree RTN (QuantizationModifier) — AutoRound (blocked — see KNOWN-FAILURES) wrapper blocked by Gemma4 qproj.linear children (see KNOWN-FAILURES). Temporary path until upstream fixed. The LLM backbone is W4A16; vision tower, projector, and PLE layers remain BF16. embedtokensperlayer and… Open weights apache-2.0 5.1B parameters 131,072 tokens transformers [View model](https://savrn.com/models/gemma4-e2b-it-w4a16) Model · Image and text to text ### [Gemma4-E2B-it-W8A16](https://savrn.com/models/gemma4-e2b-it-w8a16) [88plug AI Lab](https://savrn.com/model-publishers/88plug) These weights are compressed-tensors (pack-quantized / int-quantized). Do not deploy via TGI “text-generation-inference” paths — that backend does not load our CT format and produces opaque worker/load errors. INT8 post-training quantization of google/gemma-4-e2b-it — Google's 2B-active multimodal MoE with 128 experts. The smallest capable multimodal MoE. Runs on any 8 GB GPU. Tested with vLLM v0.21.0 (vllm/vllm-openai:v0.21.0-cu129-ubuntu2404). Weights are in compressed-tensors format — vLLM detects and loads quantization automatically. No --quantization flag needed. Weights are in compressed-tensors format — no --quantization flag needed. Requires vLLM ≥ v0.21.0. Fits entirely on an 8 GB… Open weights apache-2.0 5.1B parameters 131,072 tokens transformers [View model](https://savrn.com/models/gemma4-e2b-it-w8a16) Model · Image and text to text ### [dama-aibrain](https://savrn.com/models/dama-aibrain) [Parkseonghun](https://savrn.com/model-publishers/pshun) This gemma4 model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 5.1B parameters 131,072 tokens transformers [View model](https://savrn.com/models/dama-aibrain) Model · Image and text to text ### [cpt8](https://savrn.com/models/cpt8) [Xabxa](https://savrn.com/model-publishers/agtian) This model is a fine-tuned version of Agtian/cpt8. 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GPT-6 Luna is a closed model, called through its API. Like every… Open weights apache-2.0 4.7B parameters 262,144 tokens transformers [View model](https://savrn.com/models/p110-thaiocr-4b) ## Infomaniak Network SA [All models and datasets](https://savrn.com/model-publishers/infomaniak-ai) ## Versions - [cb3e0b2504f0](https://savrn.com/models/vllm-translategemma-4b-it/versions/cb3e0b2504f0) · current 2026-09-18 ## Explore More - [All image and text to text models](https://savrn.com/models/tasks/image-and-text-to-text) - [All models under gemma](https://savrn.com/models/licenses/gemma) - [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 2026-10-19. - [Hugging Face record](https://huggingface.co/Infomaniak-AI/vllm-translategemma-4b-it) - [How the hub is built](https://savrn.com/model-hub/methodology)