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Model garden Router · on request Dedicated · available

Qwen3.5 397B A17B FP8

Runs as your own dedicated GPU deployment, ready to launch from the wizard. Data stays in Europe.

[!Note] This repository contains FP8-quantized model weights and configuration files for the post-trained model in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. The quantization method is fin...

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Qwen/Qwen3.5-397B-A17B-FP8 On request
text->text · qwen · sovereign EU
Runs on EU infrastructure operated by European companies; US marketplace capacity is never part of this chain. Full chain
397B
Parameters
262K
Context window
488GB
Minimum VRAM
POST /api/v1/chat/completions On request

Specifications

Parameters 397B
Context window 262,144 tokens
Minimum VRAM 488 GB
Architecture Qwen3_5MoeForConditionalGeneration (vLLM)
License open-weights
Modality text->text
Released February 2026
Publisher qwen ↗

Pricing

Shared router · per token
On request
Not available on the shared router. Pricing on request as a dedicated GPU deployment.
Prefer per token on the shared router? This model runs always-on as Qwen3.5 397B A17B.
Dedicated GPU · per hour
from €21,48 per hour
Your own vLLM instance on European cloud (488 GB VRAM), billed hourly.

Shared EU router, pay-per-token, scale-to-zero. Dedicated GPU deployments are billed hourly, see pricing.

Call it now

Drop-in replacement for OpenAI: change only the base URL and API key. The Anthropic format (/v1/messages) is supported too.

curl https://hostyourai.com/api/v1/chat/completions \
  -H "Authorization: Bearer hyai-..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3.5-397B-A17B-FP8",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Frequently asked questions

Can I run Qwen3.5 397B A17B FP8 in the EU?

Yes. HostYourAI runs Qwen3.5 397B A17B FP8 on GPUs in European datacenters via vLLM. Prompts and outputs never leave the EU and there is no US cloud provider in the chain.

Is hosting Qwen3.5 397B A17B FP8 GDPR-compliant?

Yes. All processing happens inside the EU, a Data Processing Agreement (DPA) is available and the subprocessor list is public. Open-source weights also mean: no training on your data.

How much does Qwen3.5 397B A17B FP8 cost?

Qwen3.5 397B A17B FP8 is available on request: through the shared EU router after a quick chat, or as your own dedicated GPU deployment billed per hour. Tell us your use case and we will set it up for you.

Is the API OpenAI-compatible?

Yes. You use the standard OpenAI SDKs with a custom base URL (https://hostyourai.com/api/v1). The Anthropic Messages API is supported as a drop-in as well.

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The Qwen3-ASR family includes Qwen3-ASR-1.7B and Qwen3-ASR-0.6B, which support language identification and ASR for 52 languages and dialects. Both leverage large-scale speech training data and the strong audio understanding capability of their foundation model, Qwen3-Omni. The 1.7B version achieves state-of-the-art performance among open-source ASR models and is competitive with the strongest proprietary commercial APIs.

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[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

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[!Note] This repository contains model weights and configuration files for the pre-trained only model in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. The intended use cases are fine-tuning, in-context learning experiments, and other research or development purposes, not direct interaction. However, the control tokens, e.g., <|imstart| and <|imend| were trained to allow efficient LoRA-style PEFT with the official chat template, mitigating the need to finetune embeddings, a significant optimization given Qwen3.5's larger

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Qwen3.5 0.8B

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. In light of its parameter scale, the intended use cases are prototyping, task-specific fine-tuning, and other research or development purposes.

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