gpuos

Small & fast · Pro plan

Run Phi3 3.8B MINI 4K INSTRUCT · F16 on your own GPU

Phi3 3.8B MINI 4K INSTRUCT · F16 is a small & fast catalog model (3.8B) managed with Ollama. Catalog memory is estimated at 10 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 phi3-3.8b-mini-4k-instruct-fp16 through the hosted gpuos gateway, with API keys, optional token quotas and usage metering. Request and response payloads pass through that gateway.

Parameters
3.8B
Quantization
F16
Est. VRAM (4–8K ctx)
≈ 10 GB
Catalog context
4K tokens
License
MIT
Engine
Ollama
Ollama tag
phi3:3.8b-mini-4k-instruct…
API
/v1/chat/completions

Compare GPU memory for Phi3 3.8B MINI 4K INSTRUCT · 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 Phi3 3.8B MINI 4K INSTRUCT · 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="phi3-3.8b-mini-4k-instruct-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: "phi3-3.8b-mini-4k-instruct-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": "phi3-3.8b-mini-4k-instruct-fp16", "messages": [{"role": "user", "content": "Hello"}]}'

Questions

How much VRAM does Phi3 3.8B MINI 4K INSTRUCT · F16 need?
The catalog estimate is 10 GB at F16 for a short context (4–8K tokens). The RTX 4070 Ti Super 16 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 Phi3 3.8B MINI 4K INSTRUCT · F16 commercially?
Yes. Phi3 3.8B MINI 4K INSTRUCT · F16 is released under the MIT license, which allows commercial use.
Is Phi3 3.8B MINI 4K INSTRUCT · F16 compatible with the OpenAI API?
Through gpuos, Phi3 3.8B MINI 4K INSTRUCT · F16 uses the supported /v1/chat/completions endpoint with the model id "phi3-3.8b-mini-4k-instruct-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 Phi3 3.8B MINI 4K INSTRUCT · F16?
Phi3 3.8B MINI 4K INSTRUCT · F16 is part of the full catalog on the Pro plan, $29 per GPU per month.

Related models

Plan your Phi3 3.8B MINI 4K INSTRUCT · 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