gpuos

Small & fast · Free plan

Run Qwen 0.5B CHAT V1.5 · unknown on your own GPU

Qwen 0.5B CHAT V1.5 · unknown is a small & fast catalog model (620M) managed with Ollama. Catalog memory is estimated at 3.5 GB with unknown 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 qwen-0.5b-chat-v1.5-fp16 through the hosted gpuos gateway, with API keys, optional token quotas and usage metering. Request and response payloads pass through that gateway.

Parameters
620M
Quantization
unknown
Est. VRAM (4–8K ctx)
≈ 3.5 GB
Catalog context
32K tokens
License
Noncommercial terms
Engine
Ollama
Ollama tag
qwen:0.5b-chat-v1.5-fp16
API
/v1/chat/completions

This model has noncommercial restrictions. Review the publisher's terms.

Compare GPU memory for Qwen 0.5B CHAT V1.5 · unknown

Catalog estimate at unknown, 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 Qwen 0.5B CHAT V1.5 · unknown 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="qwen-0.5b-chat-v1.5-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: "qwen-0.5b-chat-v1.5-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": "qwen-0.5b-chat-v1.5-fp16", "messages": [{"role": "user", "content": "Hello"}]}'

Questions

How much VRAM does Qwen 0.5B CHAT V1.5 · unknown need?
The catalog estimate is 3.5 GB at unknown 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 Qwen 0.5B CHAT V1.5 · unknown commercially?
Qwen 0.5B CHAT V1.5 · unknown is released under the Noncommercial terms. This model has noncommercial restrictions. Review the publisher's terms.
Is Qwen 0.5B CHAT V1.5 · unknown compatible with the OpenAI API?
Through gpuos, Qwen 0.5B CHAT V1.5 · unknown uses the supported /v1/chat/completions endpoint with the model id "qwen-0.5b-chat-v1.5-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 Qwen 0.5B CHAT V1.5 · unknown?
Qwen 0.5B CHAT V1.5 · unknown is in the base catalog, available on the free Community plan (1 node, 1 GPU).

Related models

Plan your Qwen 0.5B CHAT V1.5 · unknown 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