Note
Custom model support is currently in beta. If you encounter issues or limitations, please report them via our Slack community channel or customer support.
Supported models in Managed Inference
Reviewed on 08 April 2025 • Published on 08 April 2025
Scaleway Managed Inference allows you to deploy various AI models, either from:
- Scaleway model catalog: A curated set of ready-to-deploy models available through the Scaleway console or the Managed Inference models API
- Custom models: Models that you import, typically from sources like Hugging Face.
Scaleway model catalogLink to this anchor
You can find a complete list of all models available in Scaleway’s catalog on the Managed Inference model catalog page.
Custom modelsLink to this anchor
PrerequisitesLink to this anchor
Tip
We recommend starting with a variation of a supported model from the Scaleway catalog. For example, you can deploy a quantized (4-bit) version of Llama 3.3. If deploying a fine-tuned version of Llama 3.3, make sure your file structure matches the example linked above. Examples whose compatibility has been tested are available in tested models.
To deploy a custom model via Hugging Face, ensure the following:
Access requirements
- You must have access to the model using your Hugging Face credentials.
- For gated models, request access through your Hugging Face account.
- Credentials are not stored, but we recommend using read or fine-grained access tokens.
Required files
Your model repository must include:
- A
config.jsonfile containig:- An
architecturesarray (see supported architectures for the exact list of supported values). max_position_embeddings
- An
- Model weights in the
.safetensorsformat - A
tokenizer.jsonfile- If your are fine-tuning an existing model, we recommend you use the same
tokenizer.jsonfile from the base model.
- If your are fine-tuning an existing model, we recommend you use the same
- A chat template included in either:
tokenizer_config.jsonas achat_templatefield, orchat_template.jsonas achat_templatefield
Supported model types
Your model must be one of the following types:
chatvisionmultimodal(chat + vision)embedding
Important
Security Notice
Models using formats that allow arbitrary code execution, such as Python pickle, are not supported.
API supportLink to this anchor
Depending on the model type, specific endpoints and features will be supported.
Chat modelsLink to this anchor
The Chat API will be exposed for this model under /v1/chat/completions endpoint.
Structured outputs or Function calling are not yet supported for custom models.
Vision modelsLink to this anchor
Chat API will be exposed for this model under /v1/chat/completions endpoint.
Structured outputs or Function calling are not yet supported for custom models.
Multimodal modelsLink to this anchor
These models will be treated similarly to both Chat and Vision models.
Embedding modelsLink to this anchor
Embeddings API will be exposed for this model under /v1/embeddings endpoint.
Custom model lifecycleLink to this anchor
Currently, custom model deployments are considered to be valid for the long term, and we will ensure any updates or changes to Managed Inference will not impact existing deployments. In case of breaking changes, leading to some custom models not being supported anymore, we will notify you at least 3 months beforehand.
LicensingLink to this anchor
When deploying custom models, you remain responsible for complying with any License requirements from the model provider, as you would do by running the model on a custom provisioned GPU.
Supported model architecturesLink to this anchor
Custom models must conform to one of the architectures listed below. Click to expand full list.
Supported custom model architectures
Custom model deployment currently supports the following model architectures:
AquilaModelAquilaForCausalLMArcticForCausalLMBaiChuanForCausalLMBaichuanForCausalLMBloomForCausalLMCohereForCausalLMCohere2ForCausalLMDbrxForCausalLMDeciLMForCausalLMDeepseekForCausalLMDeepseekV2ForCausalLMDeepseekV3ForCausalLMExaoneForCausalLMFalconForCausalLMFairseq2LlamaForCausalLMGemmaForCausalLMGemma2ForCausalLMGlmForCausalLMGPT2LMHeadModelGPTBigCodeForCausalLMGPTJForCausalLMGPTNeoXForCausalLMGraniteForCausalLMGraniteMoeForCausalLMGritLMInternLMForCausalLMInternLM2ForCausalLMInternLM2VEForCausalLMInternLM3ForCausalLMJAISLMHeadModelJambaForCausalLMLlamaForCausalLMLLaMAForCausalLMMambaForCausalLMFalconMambaForCausalLMMiniCPMForCausalLMMiniCPM3ForCausalLMMistralForCausalLMMixtralForCausalLMQuantMixtralForCausalLMMptForCausalLMMPTForCausalLMNemotronForCausalLMOlmoForCausalLMOlmo2ForCausalLMOlmoeForCausalLMOPTForCausalLMOrionForCausalLMPersimmonForCausalLMPhiForCausalLMPhi3ForCausalLMPhi3SmallForCausalLMPhiMoEForCausalLMQwen2ForCausalLMQwen2MoeForCausalLMRWForCausalLMStableLMEpochForCausalLMStableLmForCausalLMStarcoder2ForCausalLMSolarForCausalLMTeleChat2ForCausalLMXverseForCausalLMBartModelBartForConditionalGenerationFlorence2ForConditionalGenerationBertModelRobertaModelRobertaForMaskedLMXLMRobertaModelDeciLMForCausalLMGemma2ModelGlmForCausalLMGritLMInternLM2ForRewardModelJambaForSequenceClassificationLlamaModelMistralModelPhi3ForCausalLMQwen2ModelQwen2ForCausalLMQwen2ForRewardModelQwen2ForProcessRewardModelTeleChat2ForCausalLMLlavaNextForConditionalGenerationPhi3VForCausalLMQwen2VLForConditionalGenerationQwen2ForSequenceClassificationBertForSequenceClassificationRobertaForSequenceClassificationXLMRobertaForSequenceClassificationAriaForConditionalGenerationBlip2ForConditionalGenerationChameleonForConditionalGenerationChatGLMModelChatGLMForConditionalGenerationDeepseekVLV2ForCausalLMFuyuForCausalLMH2OVLChatModelInternVLChatModelIdefics3ForConditionalGenerationLlavaForConditionalGenerationLlavaNextForConditionalGenerationLlavaNextVideoForConditionalGenerationLlavaOnevisionForConditionalGenerationMantisForConditionalGenerationMiniCPMOMiniCPMVMolmoForCausalLMNVLM_DPaliGemmaForConditionalGenerationPhi3VForCausalLMPixtralForConditionalGenerationQWenLMHeadModelQwen2VLForConditionalGenerationQwen2_5_VLForConditionalGenerationQwen2AudioForConditionalGenerationUltravoxModelMllamaForConditionalGenerationWhisperForConditionalGenerationEAGLEModelMedusaModelMLPSpeculatorPreTrainedModel
Known compatible modelsLink to this anchor
Several models have already been verified to work on Managed Inference custom models. This list is not exhaustive and is updated gradually. Click to expand the full list.
Models verified for compatibility
The following model compatibility has been verified:
ibm-granite/granite-vision-3.2-2bibm-granite/granite-3.3-2b-instructmicrosoft/phi-4nanonets/Nanonets-OCR-sQwen/Qwen3-32B
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