gpuos

Agentic coding · Pro plan

Run Yi Coder 9B BASE · F16 on your own GPU

Yi Coder 9B BASE · F16 is a agentic coding catalog model (8.8B) managed with Ollama. Catalog memory is estimated at 20.5 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 yi-coder-9b-base-fp16 through the hosted gpuos gateway, with API keys, optional token quotas and usage metering. Request and response payloads pass through that gateway.

Parameters
8.8B
Quantization
F16
Est. VRAM (4–8K ctx)
≈ 20.5 GB
Catalog context
128K tokens
License
Apache 2.0
Engine
Ollama
Ollama tag
yi-coder:9b-base-fp16
API
/v1/chat/completions

Compare GPU memory for Yi Coder 9B BASE · 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 Yi Coder 9B BASE · 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="yi-coder-9b-base-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: "yi-coder-9b-base-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": "yi-coder-9b-base-fp16", "messages": [{"role": "user", "content": "Hello"}]}'

Questions

How much VRAM does Yi Coder 9B BASE · F16 need?
The catalog estimate is 20.5 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 Yi Coder 9B BASE · F16 commercially?
Yes. Yi Coder 9B BASE · F16 is released under the Apache 2.0 license, which allows commercial use.
Is Yi Coder 9B BASE · F16 compatible with the OpenAI API?
Through gpuos, Yi Coder 9B BASE · F16 uses the supported /v1/chat/completions endpoint with the model id "yi-coder-9b-base-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 Yi Coder 9B BASE · F16?
Yi Coder 9B BASE · F16 is part of the full catalog on the Pro plan, $29 per GPU per month.

Related models

Plan your Yi Coder 9B BASE · 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