gpuos

Embeddings · Free plan

Run Qwen3 Embedding 4B · Q4_K_M on your own GPU

Qwen3 Embedding 4B · Q4_K_M is a embeddings catalog model (4.0B) managed with Ollama. Catalog memory is estimated at 3.5 GB with Q4_K_M for short inputs. 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 qwen3-embedding-4b-q4-k-m through the hosted gpuos gateway, with API keys, optional token quotas and usage metering. Request and response payloads pass through that gateway.

Parameters
4.0B
Quantization
Q4_K_M
Est. VRAM (short inputs)
≈ 3.5 GB
Catalog context
40K tokens
License
Upstream terms
Engine
Ollama
Ollama tag
qwen3-embedding:4b-q4_K_M
API
/v1/embeddings

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

Compare GPU memory for Qwen3 Embedding 4B · Q4_K_M

Catalog estimate at Q4_K_M, 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 Qwen3 Embedding 4B · Q4_K_M with the OpenAI SDK

Use the supported embeddings 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_…")
result = client.embeddings.create(model="qwen3-embedding-4b-q4-k-m", input=["first text", "second text"])
print(len(result.data[0].embedding))
Node.js
import OpenAI from "openai"

const client = new OpenAI({ baseURL: "https://gpuos.si/v1", apiKey: process.env.GPUOS_API_KEY })
const result = await client.embeddings.create({ model: "qwen3-embedding-4b-q4-k-m", input: "first text" })
curl
curl https://gpuos.si/v1/embeddings \
  -H "Authorization: Bearer $GPUOS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "qwen3-embedding-4b-q4-k-m", "input": "first text"}'

Questions

How much VRAM does Qwen3 Embedding 4B · Q4_K_M need?
The catalog estimate is 3.5 GB at Q4_K_M for short inputs. 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 Qwen3 Embedding 4B · Q4_K_M commercially?
Qwen3 Embedding 4B · Q4_K_M is released under the Upstream terms. Review the publisher's license before deployment. Rights have not been classified automatically.
Is Qwen3 Embedding 4B · Q4_K_M compatible with the OpenAI API?
Through gpuos, Qwen3 Embedding 4B · Q4_K_M uses the supported /v1/embeddings endpoint with the model id "qwen3-embedding-4b-q4-k-m". 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 Qwen3 Embedding 4B · Q4_K_M?
Qwen3 Embedding 4B · Q4_K_M is in the base catalog, available on the free Community plan (1 node, 1 GPU).

Related models

Plan your Qwen3 Embedding 4B · Q4_K_M 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