gpuos

Chat · Pro plan

Run Internlm2 20B CHAT V2.5 · Q4_K_M on your own GPU

Internlm2 20B CHAT V2.5 · Q4_K_M is a chat catalog model (19.9B) managed with Ollama. Catalog memory is estimated at 14.5 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 internlm2-20b-chat-v2.5-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
19.9B
Quantization
Q4_K_M
Est. VRAM (4–8K ctx)
≈ 14.5 GB
Catalog context
32K tokens
License
Apache 2.0
Engine
Ollama
Ollama tag
internlm2:20b-chat-v2.5-q4…
API
/v1/chat/completions

Compare GPU memory for Internlm2 20B CHAT V2.5 · 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 Internlm2 20B CHAT V2.5 · 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="internlm2-20b-chat-v2.5-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: "internlm2-20b-chat-v2.5-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": "internlm2-20b-chat-v2.5-q4-k-m", "messages": [{"role": "user", "content": "Hello"}]}'

Questions

How much VRAM does Internlm2 20B CHAT V2.5 · Q4_K_M need?
The catalog estimate is 14.5 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 Internlm2 20B CHAT V2.5 · Q4_K_M commercially?
Yes. Internlm2 20B CHAT V2.5 · Q4_K_M is released under the Apache 2.0 license, which allows commercial use.
Is Internlm2 20B CHAT V2.5 · Q4_K_M compatible with the OpenAI API?
Through gpuos, Internlm2 20B CHAT V2.5 · Q4_K_M uses the supported /v1/chat/completions endpoint with the model id "internlm2-20b-chat-v2.5-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 Internlm2 20B CHAT V2.5 · Q4_K_M?
Internlm2 20B CHAT V2.5 · Q4_K_M is part of the full catalog on the Pro plan, $29 per GPU per month.

Related models

Plan your Internlm2 20B CHAT V2.5 · 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.

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