gpuos

Agentic coding · Pro plan

Run Codellama 34B CODE · Q4_K_M on your own GPU

Codellama 34B CODE · Q4_K_M is a agentic coding catalog model (34B) managed with Ollama. Catalog memory is estimated at 23 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 codellama-34b-code-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
34B
Quantization
Q4_K_M
Est. VRAM (4–8K ctx)
≈ 23 GB
Catalog context
16K tokens
License
Llama 2 Community
Engine
Ollama
Ollama tag
codellama:34b-code-q4_K_M
API
/v1/chat/completions

Review Meta's community license, acceptable use policy and eligibility requirements.

Compare GPU memory for Codellama 34B CODE · 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 Codellama 34B CODE · 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="codellama-34b-code-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: "codellama-34b-code-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": "codellama-34b-code-q4-k-m", "messages": [{"role": "user", "content": "Hello"}]}'

Questions

How much VRAM does Codellama 34B CODE · Q4_K_M need?
The catalog estimate is 23 GB at Q4_K_M for a short context (4–8K tokens). The RTX 5090 32 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 Codellama 34B CODE · Q4_K_M commercially?
Codellama 34B CODE · Q4_K_M is released under the Llama 2 Community. Review Meta's community license, acceptable use policy and eligibility requirements.
Is Codellama 34B CODE · Q4_K_M compatible with the OpenAI API?
Through gpuos, Codellama 34B CODE · Q4_K_M uses the supported /v1/chat/completions endpoint with the model id "codellama-34b-code-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 Codellama 34B CODE · Q4_K_M?
Codellama 34B CODE · Q4_K_M is part of the full catalog on the Pro plan, $29 per GPU per month.

Related models

Plan your Codellama 34B CODE · 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