gpuos

Embeddings · Free plan

Run BGE Large 335M · F16 on your own GPU

BGE Large 335M · F16 is a embeddings catalog model (334.09M) managed with Ollama. Catalog memory is estimated at 1.5 GB with F16 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 bge-large-335m through the hosted gpuos gateway, with API keys, optional token quotas and usage metering. Request and response payloads pass through that gateway.

Parameters
334.09M
Quantization
F16
Est. VRAM (short inputs)
≈ 1.5 GB
Catalog context
1K tokens
License
MIT
Engine
Ollama
Ollama tag
bge-large:335m
API
/v1/embeddings

Compare GPU memory for BGE Large 335M · 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 BGE Large 335M · F16 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="bge-large-335m", 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: "bge-large-335m", input: "first text" })
curl
curl https://gpuos.si/v1/embeddings \
  -H "Authorization: Bearer $GPUOS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "bge-large-335m", "input": "first text"}'

Questions

How much VRAM does BGE Large 335M · F16 need?
The catalog estimate is 1.5 GB at F16 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 BGE Large 335M · F16 commercially?
Yes. BGE Large 335M · F16 is released under the MIT license, which allows commercial use.
Is BGE Large 335M · F16 compatible with the OpenAI API?
Through gpuos, BGE Large 335M · F16 uses the supported /v1/embeddings endpoint with the model id "bge-large-335m". 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 BGE Large 335M · F16?
BGE Large 335M · F16 is in the base catalog, available on the free Community plan (1 node, 1 GPU).

Related models

Plan your BGE Large 335M · 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