What this model family is
Llama Fine-Tuned Specifically for Software Development Workflows
Code Llama is a family of large language models built on Meta's Llama base and trained specifically on code. The training process produces a model that understands code structure, syntax patterns, debugging logic, and software engineering context in a way that a general-purpose model does not.
The most important property of Code Llama for a private deployment is not its benchmark position — it is what it means to run your coding assistant on your own server. Developers and agencies using cloud coding AI tools are, in many cases, routing their proprietary codebase through a third-party inference provider. For companies with competitive code, NDAs, or client confidentiality requirements, that is not acceptable. Code Llama on NoCloudGPT infrastructure solves this.
Code Llama Base
Code generation and completion
The foundation model. Strong at code completion, fill-in-the-middle, and generating code from context. Supports Python, JavaScript, TypeScript, Go, Rust, SQL, and many other languages. Available at 7B, 13B, 34B, and 70B.
Code Llama Instruct
Instruction-following for developers
Fine-tuned for conversational coding: "explain this function," "write a test for this class," "refactor to remove the bug." The recommended variant for interactive developer assistance via OpenWebUI or API integration.
Code Llama Python
Python-specialized variant
Trained with additional Python emphasis. Best for data engineering, ML pipelines, Django/FastAPI development, and scientific computing teams whose primary language is Python.
Note on newer options
Consider DeepSeek Coder or Qwen Coder
For new coding deployments, DeepSeek Coder V2 and Qwen2.5 Coder have shown strong performance on coding benchmarks. NoCloudGPT can help you compare options for your specific language and task profile.
Who developed it
Meta AI Research
Code Llama was released by Meta AI Research in August 2023 as a set of code-specialized models fine-tuned from the Llama 2 base. Meta has since released updated variants. The Code Llama family is released under the Llama 2 Community License, which permits commercial use for most organizations. Verify current license terms at the Hugging Face model card before deployment, particularly for large-scale commercial applications.
NoCloudGPT is independent of Meta AI. We help organizations deploy Code Llama on their own infrastructure using Ollama.
Who this model family is for
Who This Model Family is For
CodeLlama is a solid choice for solo developers who want a capable local coding assistant that runs entirely on their own hardware. It works well for personal side projects, learning new programming languages, writing scripts in Python, JavaScript, or Bash, and getting help with everyday coding tasks. The 7B and 13B versions run comfortably on consumer hardware. For stronger coding performance in many current deployments, newer models like DeepSeek V3 and Qwen 2.5-Coder generally deliver stronger results.
CodeLlama remains a practical option for teams that need a private, in-house coding assistant without relying on newer, larger models. It can support internal tools, code review helpers, documentation generation, and lightweight developer productivity workflows. For production-grade coding assistance in 2026, newer open models such as Qwen 2.5-Coder and Codestral typically produce stronger results. CodeLlama is best when stability, a well-understood license, and modest hardware requirements are priorities.
This is where CodeLlama still shines. The Instruct variants follow coding instructions reliably, and the base models support fill-in-the-middle (FIM) infilling, making them suitable for editor integrations, autocomplete tools, and refactoring assistants. Developers building local copilots, IDE plugins, or experimenting with agentic coding workflows will find CodeLlama predictable and well-supported by existing community tooling.
CodeLlama remains a useful baseline for code-generation research, ablation studies, and comparative evaluations. It is one of the most-cited open coding models and integrates cleanly with established benchmarks like HumanEval and MBPP. For cutting-edge performance research in 2026, you will likely also want to evaluate DeepSeek-Coder V3 and Qwen 2.5-Coder alongside it.
Licensing information
Licensing Information
CodeLlama is released by Meta under the Llama 2 Community License, which permits commercial use for most organizations. The main restriction is that if your product or service has more than 700 million monthly active users, you must request a separate license from Meta.
You can review the full license and model details on the official model card.
Official source:
Disclaimer: NoCloudGPT provides this licensing summary as a convenience reference only. It does not constitute legal advice. License terms can change. Always review the official model card and consult your legal counsel before deploying any model in a commercial or regulated context.
Available model sizes
Available Model Sizes
| Model Size | Ollama Tag | Recommended NoCloudGPT Tier | Notes | |
|---|---|---|---|---|
| CodeLlama 7B | codellama:7b |
Starter | Runs on ~8 GB VRAM. Good for autocomplete, simple scripts, and lightweight editor integrations. | Get quote → |
| CodeLlama 13B | codellama:13b |
Standard | ~16 GB VRAM. Better at multi-file reasoning and longer functions. A balanced choice for most developers. | Get quote → |
| CodeLlama 34B | codellama:34b |
Professional | ~24–32 GB VRAM. Strongest CodeLlama for general programming tasks. | Get quote → |
| CodeLlama 70B | codellama:70b |
Professional / Enterprise | Requires 48+ GB VRAM or multi-GPU. Highest quality in the family, but consider whether a newer coding model would serve you better. | Get quote → |
Note: Each size is available in Base, Instruct, and Python variants on Ollama. Use -instruct for chat-style coding assistants and the base variants for fill-in-the-middle (FIM) / autocomplete use cases. CodeLlama is an older model family (2023) and has been surpassed on most coding benchmarks by newer models such as DeepSeek V3, Qwen 2.5-Coder, and Codestral.
Why teams choose it
The Private Coding AI Argument
IP stays on your server
Startups, agencies, and defense contractors whose source code is a competitive asset cannot send it to a cloud AI provider. Code Llama on your server means your codebase never leaves your infrastructure. This is not a preference — for many organizations it is a contractual obligation.
NDA-bound development work
Consultants and agencies working under non-disclosure agreements who need AI assistance for client code have no safe option with cloud coding assistants. Private deployment removes third-party exposure entirely.
Full codebase context without limits
Run your entire codebase through Code Llama for code review, refactoring suggestions, documentation generation, and test writing — without the per-token billing that makes large-context cloud AI usage expensive at scale.
Available at sizes from team laptop to GPU server
The 7B variant runs on a 4–8 GB instance for individual developers or small teams. The 34B and 70B variants on GPU hardware provide output quality that handles complex architectural questions and code review at production scale.
Which size should you choose?
Matching Code Llama Size to Your Development Workflow
Code Llama 7B — Standard AI
Individual dev / small teamRuns on a 4–8 GB instance. Good for code completion, simple explanation, and basic debugging in well-known languages. The right starting point for individual developers or small teams testing private coding AI. Instruct variant recommended for interactive use.
Code Llama 13B — Standard/Professional AI
Better qualityNoticeably better code quality and reasoning than 7B. Handles more complex refactoring, multi-file context, and code review tasks. Requires 8–10 GB RAM at Q4. The practical upgrade step for teams that outgrow the 7B.
Code Llama 34B — Professional AI
Production teamsStrong performance on complex architectural questions, multi-step code generation, and code review at scale. Requires a GPU instance (approximately 20–24 GB VRAM at Q4). The recommended tier for production engineering teams with regular AI-assisted development workflows.
Honest guidance
When to Choose a Different Model Instead
You need the strongest current coding benchmark performance
DeepSeek Coder V2 and Qwen2.5 Coder have shown strong results on current coding benchmarks. For new deployments, compare all three options with NoCloudGPT before committing. See DeepSeek Coder →
Your primary use case is natural language reasoning, not code
Code Llama trades some general reasoning ability for coding specialization. For teams that need AI assistance for both code and general business writing, Llama 3.1 or Qwen may serve both use cases better from a single deployment. See Llama →
Deploy with NoCloudGPT
Start Your Private Code Llama Deployment
NoCloudGPT installs Code Llama on your Lightsail instance or on-premise server, configures Ollama and OpenWebUI, and selects the right variant and size for your development workflow. We can also help you compare Code Llama against DeepSeek Coder and Qwen Coder for your specific language stack.