Run Exaone Deep 32B · F16 on your own GPU

Compare GPU memory for Exaone Deep 32B · F16

Catalog estimate at F16, not a measured benchmark. Chat estimates assume a short context; embedding memory depends on input and batch size. The comparison includes a 5% screening margin; it does not establish runtime performance. More in how much VRAM an LLM needs.

Check this estimate against your GPU with the VRAM calculator

• RTX 4060 Ti 8 GBMore headroom needed
• RTX 4070 Ti Super 16 GBMore headroom needed
• RTX 4000 Ada 20 GBMore headroom needed
• RTX 3090 / 4090 24 GBMore headroom needed
• RTX PRO 4000 Blackwell 24 GB (Hetzner GEX45)More headroom needed
• NVIDIA L4 24 GBMore headroom needed
• RTX 5090 32 GBMore headroom needed
• A100 / H100 80 GBEstimate fits
• RTX PRO 6000 Blackwell 96 GB (Hetzner GEX131)Estimate fits

Call Exaone Deep 32B · F16 with the OpenAI SDK

Use the supported chat completions endpoint with your gpuos base URL, API key and deployed model id. Validate any model-specific features your app needs. Setup for LangChain, Continue, Open WebUI and more is in integrations.

from openai import OpenAI

client = OpenAI(base_url="https://gpuos.si/v1", api_key="gpuos_key_…")
stream = client.chat.completions.create(
    model="exaone-deep-32b-fp16",
    messages=[{"role": "user", "content": "Summarize our refund policy."}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

import OpenAI from "openai"

const client = new OpenAI({ baseURL: "https://gpuos.si/v1", apiKey: process.env.GPUOS_API_KEY })
const reply = await client.chat.completions.create({
  model: "exaone-deep-32b-fp16",
  messages: [{ role: "user", content: "Summarize our refund policy." }],
})
console.log(reply.choices[0].message.content)

curl https://gpuos.si/v1/chat/completions \
  -H "Authorization: Bearer $GPUOS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "exaone-deep-32b-fp16", "messages": [{"role": "user", "content": "Hello"}]}'

Questions

How much VRAM does Exaone Deep 32B · F16 need?

The catalog estimate is 68 GB at F16 for a short context (4–8K tokens). The A100 / H100 80 GB clears this estimate with screening headroom in the comparison list. This is a planning estimate, not a tested hardware requirement. Validate context length, batch size and concurrent requests on your node.

Can I use Exaone Deep 32B · F16 commercially?

Exaone Deep 32B · F16 is released under the Modified MIT. Revenue-based commercial restrictions apply. Review the publisher's commercial license requirements.

Is Exaone Deep 32B · F16 compatible with the OpenAI API?

Through gpuos, Exaone Deep 32B · F16 uses the supported /v1/chat/completions endpoint with the model id "exaone-deep-32b-fp16". Configure your OpenAI-compatible client with the gpuos base URL and API key. Check model-specific features and any other API operations your app needs before switching.

Which gpuos plan includes Exaone Deep 32B · F16?

Exaone Deep 32B · F16 is part of the full catalog on the Pro plan, $29 per GPU per month.

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