gpuos

Agentic coding · Free plan

Run Opencoder 1.5B INSTRUCT · Q8_0 on your own GPU

Opencoder 1.5B INSTRUCT · Q8_0 is a agentic coding catalog model (1.9B) managed with Ollama. Catalog memory is estimated at 4.5 GB with Q8_0 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 opencoder-1.5b-instruct-q8-0 through the hosted gpuos gateway, with API keys, optional token quotas and usage metering. Request and response payloads pass through that gateway.

Parameters
1.9B
Quantization
Q8_0
Est. VRAM (4–8K ctx)
≈ 4.5 GB
Catalog context
4K tokens
License
Upstream terms
Engine
Ollama
Ollama tag
opencoder:1.5b-instruct-q8_0
API
/v1/chat/completions

Review the publisher's license before deployment. Rights have not been classified automatically.

Compare GPU memory for Opencoder 1.5B INSTRUCT · Q8_0

Catalog estimate at Q8_0, 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 Opencoder 1.5B INSTRUCT · Q8_0 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="opencoder-1.5b-instruct-q8-0",
    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: "opencoder-1.5b-instruct-q8-0",
  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": "opencoder-1.5b-instruct-q8-0", "messages": [{"role": "user", "content": "Hello"}]}'

Questions

How much VRAM does Opencoder 1.5B INSTRUCT · Q8_0 need?
The catalog estimate is 4.5 GB at Q8_0 for a short context (4–8K tokens). The RTX 4060 Ti 8 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 Opencoder 1.5B INSTRUCT · Q8_0 commercially?
Opencoder 1.5B INSTRUCT · Q8_0 is released under the Upstream terms. Review the publisher's license before deployment. Rights have not been classified automatically.
Is Opencoder 1.5B INSTRUCT · Q8_0 compatible with the OpenAI API?
Through gpuos, Opencoder 1.5B INSTRUCT · Q8_0 uses the supported /v1/chat/completions endpoint with the model id "opencoder-1.5b-instruct-q8-0". 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 Opencoder 1.5B INSTRUCT · Q8_0?
Opencoder 1.5B INSTRUCT · Q8_0 is in the base catalog, available on the free Community plan (1 node, 1 GPU).

Related models

Plan your Opencoder 1.5B INSTRUCT · Q8_0 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