gpuos

Chat · Pro plan

Run Gemma2 9B INSTRUCT · F16 on your own GPU

Gemma2 9B INSTRUCT · F16 is a chat catalog model (9.2B) managed with Ollama. Catalog memory is estimated at 21 GB with F16 for a short context (4–8K tokens). Validate output quality and memory use with your workload on your hardware.

Deploy the catalog Ollama tag on your machine and verify the pulled model's quantization. Once deployed, gpuos serves it as gemma2-9b-instruct-fp16 through the hosted gpuos gateway, with API keys, optional token quotas and usage metering. Request and response payloads pass through that gateway.

Parameters
9.2B
Quantization
F16
Est. VRAM (4–8K ctx)
≈ 21 GB
Catalog context
8K tokens
License
Gemma terms
Engine
Ollama
Ollama tag
gemma2:9b-instruct-fp16
API
/v1/chat/completions

Review Google's Gemma terms and prohibited use policy.

Compare GPU memory for Gemma2 9B INSTRUCT · 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

Call Gemma2 9B INSTRUCT · 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.

Python
from openai import OpenAI

client = OpenAI(base_url="https://gpuos.si/v1", api_key="gpuos_key_…")
stream = client.chat.completions.create(
    model="gemma2-9b-instruct-fp16",
    messages=[{"role": "user", "content": "Summarize our refund policy."}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")
Node.js
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: "gemma2-9b-instruct-fp16",
  messages: [{ role: "user", content: "Summarize our refund policy." }],
})
console.log(reply.choices[0].message.content)
curl
curl https://gpuos.si/v1/chat/completions \
  -H "Authorization: Bearer $GPUOS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "gemma2-9b-instruct-fp16", "messages": [{"role": "user", "content": "Hello"}]}'

Questions

How much VRAM does Gemma2 9B INSTRUCT · F16 need?
The catalog estimate is 21 GB at F16 for a short context (4–8K tokens). The RTX 3090 / 4090 24 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 Gemma2 9B INSTRUCT · F16 commercially?
Gemma2 9B INSTRUCT · F16 is released under the Gemma terms. Review Google's Gemma terms and prohibited use policy.
Is Gemma2 9B INSTRUCT · F16 compatible with the OpenAI API?
Through gpuos, Gemma2 9B INSTRUCT · F16 uses the supported /v1/chat/completions endpoint with the model id "gemma2-9b-instruct-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 Gemma2 9B INSTRUCT · F16?
Gemma2 9B INSTRUCT · F16 is part of the full catalog on the Pro plan, $29 per GPU per month.

Related models

Plan your Gemma2 9B INSTRUCT · F16 deployment with gpuOS

Create a free workspace and follow the quickstart to connect a node. Start with a Community model, or upgrade inside your workspace for the full catalog.

Start freeRead the quickstart