gpuos

Chat · Pro plan

Run Mistral 7B TEXT V0.2 · F16 on your own GPU

Mistral 7B TEXT V0.2 · F16 is a chat catalog model (7B) managed with Ollama. Catalog memory is estimated at 17 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 mistral-7b-text-v0.2-fp16 through the hosted gpuos gateway, with API keys, optional token quotas and usage metering. Request and response payloads pass through that gateway.

Parameters
7B
Quantization
F16
Est. VRAM (4–8K ctx)
≈ 17 GB
Catalog context
16K tokens
License
Apache 2.0
Engine
Ollama
Ollama tag
mistral:7b-text-v0.2-fp16
API
/v1/chat/completions

Compare GPU memory for Mistral 7B TEXT V0.2 · 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 Mistral 7B TEXT V0.2 · 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="mistral-7b-text-v0.2-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: "mistral-7b-text-v0.2-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": "mistral-7b-text-v0.2-fp16", "messages": [{"role": "user", "content": "Hello"}]}'

Questions

How much VRAM does Mistral 7B TEXT V0.2 · F16 need?
The catalog estimate is 17 GB at F16 for a short context (4–8K tokens). The RTX 4000 Ada 20 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 Mistral 7B TEXT V0.2 · F16 commercially?
Yes. Mistral 7B TEXT V0.2 · F16 is released under the Apache 2.0 license, which allows commercial use.
Is Mistral 7B TEXT V0.2 · F16 compatible with the OpenAI API?
Through gpuos, Mistral 7B TEXT V0.2 · F16 uses the supported /v1/chat/completions endpoint with the model id "mistral-7b-text-v0.2-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 Mistral 7B TEXT V0.2 · F16?
Mistral 7B TEXT V0.2 · F16 is part of the full catalog on the Pro plan, $29 per GPU per month.

Related models

Plan your Mistral 7B TEXT V0.2 · 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