Code Llama is an open-source AI model developed by Meta. It can be deployed on-premises with the right GPU hardware for private, secure AI inference.

How much VRAM does Code Llama require?

VRAM requirements for Code Llama depend on the quantization level. Full-precision models need more VRAM, while quantized versions (Q4, Q5, Q8) can run on consumer GPUs. See our VRAM requirements table for specific recommendations.

Can I run Code Llama locally?

Yes. Code Llama can be run locally using frameworks like Ollama or vLLM. Petronella Technology Group builds GPU-accelerated workstations and servers optimized for local AI model deployment.

What GPU do I need for Code Llama?

The recommended GPU depends on the model size and quantization. For smaller quantized versions, an AMD Radeon or NVIDIA RTX GPU with 16-24 GB VRAM may suffice. For full-precision or larger variants, enterprise GPUs like the AMD Instinct MI300X or NVIDIA A100 are recommended.

Does Petronella help deploy Code Llama?

Yes. Petronella Technology Group provides end-to-end AI deployment services including hardware selection, system configuration, model optimization, and ongoing support. Contact us to discuss your Code Llama deployment needs.

Open-Source AI Model

Code Llama

Name: Code Llama
Author: Meta

Developed by Meta

Local AI Deployment Experts 24+ Years IT Infrastructure GPU Hardware In Stock

Key Capabilities

Code generation across 85+ programming languages
100K context for large codebase understanding
Fill-in-the-middle (infilling) for code completion
Instruction-tuned variant for coding assistants
Python-specialized variant (Code Llama Python)

VRAM Requirements by Quantization

Choose the right GPU based on your performance and quality needs.

Model / Quantization	VRAM Required
7B FP16	14GB
34B FP16	68GB
70B FP16	140GB
70B Q4	40GB

Use Cases

Code Llama (7B, 13B, 34B, 70B) can be deployed for enterprise AI applications including document processing, code generation, data analysis, and conversational AI. License: Meta Code Llama Community License (commercial use allowed).

Run Code Llama with Petronella

PTG deploys Code Llama for software teams needing private code completion and generation. Run your coding AI on-premises where proprietary code never leaves your network. Essential for CMMC compliance.

Recommended Hardware

Model Size	Recommended GPU
7B	RTX 5080 (16GB)
34B	RTX 5090 (32GB) or RTX PRO 5000 (48GB)
70B	RTX PRO 6000 Blackwell (96GB)

Deploy Code Llama On-Premises

Our team builds GPU-accelerated systems configured and optimized for Code Llama. Private, secure, and fully under your control.

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Code Llama

⚡Key Capabilities

📌VRAM Requirements by Quantization

🚀Use Cases