gpuos

Vision · Pro plan

Run Ministral 3 8B INSTRUCT 2512 · Q4_K_M on your own GPU

Ministral 3 8B INSTRUCT 2512 · Q4_K_M is a vision catalog model (8.9B) managed with Ollama. Catalog memory is estimated at 8.5 GB with Q4_K_M 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 ministral-3-8b-instruct-2512-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
8.9B
Quantization
Q4_K_M
Est. VRAM (4–8K ctx)
≈ 8.5 GB
Catalog context
256K tokens
License
Apache 2.0
Engine
Ollama
Ollama tag
ministral-3:8b-instruct-25…
API
/v1/chat/completions

Compare GPU memory for Ministral 3 8B INSTRUCT 2512 · 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 Ministral 3 8B INSTRUCT 2512 · Q4_K_M 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="ministral-3-8b-instruct-2512-q4-k-m",
    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: "ministral-3-8b-instruct-2512-q4-k-m",
  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": "ministral-3-8b-instruct-2512-q4-k-m", "messages": [{"role": "user", "content": "Hello"}]}'

Questions

How much VRAM does Ministral 3 8B INSTRUCT 2512 · Q4_K_M need?
The catalog estimate is 8.5 GB at Q4_K_M 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 Ministral 3 8B INSTRUCT 2512 · Q4_K_M commercially?
Yes. Ministral 3 8B INSTRUCT 2512 · Q4_K_M is released under the Apache 2.0 license, which allows commercial use.
Is Ministral 3 8B INSTRUCT 2512 · Q4_K_M compatible with the OpenAI API?
Through gpuos, Ministral 3 8B INSTRUCT 2512 · Q4_K_M uses the supported /v1/chat/completions endpoint with the model id "ministral-3-8b-instruct-2512-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 Ministral 3 8B INSTRUCT 2512 · Q4_K_M?
Ministral 3 8B INSTRUCT 2512 · Q4_K_M is part of the full catalog on the Pro plan, $29 per GPU per month.

Related models

Plan your Ministral 3 8B INSTRUCT 2512 · 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