WIDEAREAINTELLIGENCE
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huggingface

Qwen3.8 9B Distill

hf/empero-ai/Qwen3.8-9B-Distill-GGUF

↓ runs free on your own hardware⚙ tool calling

Open-weight model — runs free on your own GPU via the node runtime (GGUF). 679,221 downloads and 281 likes on Hugging Face (empero-ai/Qwen3.8-9B-Distill-GGUF).

specs & pricing
Type
text
Provider
huggingface
Model ID
hf/empero-ai/Qwen3.8-9B-Distill-GGUF
Capabilities
tools
Self-hostable
Yes — runs on your own GPU

Listed in the directory for discovery. textmodels aren't callable through the chat gateway yet — image and video models can run on your own nodes today.

Run it on your own hardware

Estimated GPU memory for Qwen3.8 9B Distill (9.0B parameters), for the weights alone — add room for the context cache.

Q4_K_M
~5.9 GBfits a 8 GB card
Q8_0
~9.6 GBfits a 12 GB card
FP16 / BF16
~17.5 GBfits a 24 GB card

Install the agent on the machine and it dials out to the gateway over a single WebSocket — no port forwarding or inbound firewall rules. Requests for this model go to your node first and fail over to the cloud only when it can't serve.

VRAM calculator →Which GPU do I need? →

/// initialize

Call Qwen3.8 9B Distill through one OpenAI-compatible endpoint.

Serve it from hardware you own, with cloud failover when your nodes are busy or offline.

Run it on your own hardware

no credit card · 2 nodes free · openai-compatible

Frequently asked questions

Can I run Qwen3.8 9B Distill on my own hardware?
Yes — Qwen3.8 9B Distill is an open-weight model you can run on a workstation or on-prem server you own. At Q4_K_M it needs roughly 5.9 GB of GPU memory, plus room for the context cache — it fits a single 8 GB card. Wide Area Intelligence routes requests to your own nodes first and fails over to the cloud only when they can't serve.

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