Code Llama 13B Instruct by Meta is a powerful 13 billion parameter model designed specifically for code generation and instruction-following tasks. This model excels in generating high-quality, contextually relevant code snippets across various programming languages, making it an excellent choice for developers and software engineers who need to automate coding tasks or generate boilerplate code quickly. With a context length of 16384 tokens, it can handle complex and lengthy codebases, ensuring that the generated code remains coherent and contextually accurate.
Compared to other models in its size class, Code Llama 13B Instruct punches well above its weight. It offers a balance between performance and efficiency, making it a strong contender for those who need robust code generation capabilities without the need for extremely high-end hardware. The model's quantization options, such as Q4_K_M, further enhance its efficiency, allowing it to run smoothly on systems with as little as 7.8 GB of VRAM. This makes it accessible to a wide range of users, from hobbyists with mid-range GPUs to professionals with more powerful setups. Ideal users include developers looking to speed up their coding process, researchers working on code-related projects, and anyone who needs to generate or modify code efficiently.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 7.326 GB | 7.83 GB | 8.33 GB | 85% |
Context window & KV cache
Adds 1.25 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 16K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Code Llama 13B Instruct
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Easiest. Single command. OpenAI-compatible API on :11434.
Ollama home →- 1
Pull the model
ollama pull codellama:13b-instruct - 2
Chat
ollama run codellama:13b-instruct - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"codellama:13b-instruct","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Code Llama 13B Instruct on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Code Llama 13B Instructfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
9.2 GB
7.8 GB weights + 0.9 GB KV
Aggregate tok/s
19
across 1 user
Per-user tok/s
19
13 B dense
✅ Fits in 24 GB VRAM with 14.8 GB headroom. Pure-GPU inference — full speed.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run Code Llama 13B Instruct?
Code Llama 13B Instruct requires 7.83 GB VRAM minimum with Q4_K_M quantization. For full precision you need 7.83 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Code Llama 13B Instruct?
To run Code Llama 13B Instruct, you need a GPU with at least 7.8 GB of VRAM. NVIDIA GPUs like the RTX 3090 or RTX 4090 are recommended for optimal performance.
Is Code Llama 13B Instruct good for coding?
Yes, Code Llama 13B Instruct is specifically designed for complex coding tasks and can provide high-quality code generation and assistance.
Code Llama 13B Instruct vs Llama 3.1 8B?
Code Llama 13B Instruct has more parameters (13B vs 8B), which generally results in better performance for complex tasks, but it requires more VRAM and computational resources.
Can I run Code Llama 13B Instruct on a Mac?
Yes, you can run Code Llama 13B Instruct on a Mac with an M1 or M2 chip, but performance may vary. Ensure your Mac has sufficient VRAM and consider using a compatible GPU for better results.
How much VRAM does Code Llama 13B Instruct need?
Code Llama 13B Instruct requires 7.8 GB of VRAM. This is the minimum requirement to run the model, but more VRAM can improve performance and allow for larger batch sizes.
Is Code Llama 13B Instruct censored?
Code Llama 13B Instruct is not inherently censored, but it adheres to ethical guidelines and content policies set by Meta to ensure responsible use.
Is Code Llama 13B Instruct commercial-use allowed?
Yes, Code Llama 13B Instruct is licensed under the llama2 license, which allows for commercial use as long as you comply with the terms of the license.
Code Llama 13B Instruct context length?
The context length for Code Llama 13B Instruct is 16,384 tokens, allowing for very long input sequences and complex tasks.
Does Code Llama 13B Instruct support function calling?
Yes, Code Llama 13B Instruct supports function calling, enabling it to interact with external systems and perform more dynamic tasks.
Code Llama 13B Instruct quantization options?
Code Llama 13B Instruct supports quantization options such as 4-bit and 8-bit, which can reduce the model size and VRAM usage while maintaining acceptable performance.
Can Code Llama 13B Instruct run on CPU?
While Code Llama 13B Instruct can technically run on a CPU, it is highly recommended to use a GPU for better performance due to the model's large size and computational demands.
Code Llama 13B Instruct fine-tuning?
Yes, Code Llama 13B Instruct can be fine-tuned on custom datasets to improve its performance on specific tasks or domains.
Code Llama 13B Instruct system requirements?
To run Code Llama 13B Instruct, you need a system with at least 7.8 GB of VRAM, a powerful CPU, and at least 50 GB of free disk space for the model files.
Code Llama 13B Instruct performance benchmark?
Performance benchmarks for Code Llama 13B Instruct show it can process around 20-30 tokens per second on a high-end GPU like the RTX 4090, depending on the task complexity and batch size.
Code Llama 13B Instruct for RAG?
Yes, Code Llama 13B Instruct can be used for Retrieval-Augmented Generation (RAG) tasks, combining its strong language capabilities with external knowledge sources.
Code Llama 13B Instruct for agents?
Code Llama 13B Instruct can be integrated into agent systems to provide advanced natural language understanding and generation capabilities, enhancing the agent's performance.
Code Llama 13B Instruct for coding vs general?
Code Llama 13B Instruct is optimized for coding tasks, providing specialized knowledge and context-aware assistance, while general-purpose models may offer broader but less specialized capabilities.
Code Llama 13B Instruct vs ChatGPT?
Code Llama 13B Instruct is specifically tailored for coding tasks and has a longer context length (16,384 tokens), while ChatGPT is a more general-purpose model with a shorter context length (4,096 tokens).
Code Llama 13B Instruct download size?
The download size for Code Llama 13B Instruct is approximately 50 GB, depending on the quantization level and additional files.
Best quant for Code Llama 13B Instruct?
The best quantization for Code Llama 13B Instruct depends on your hardware and performance needs. 8-bit quantization offers a good balance between model size and performance, while 4-bit can significantly reduce VRAM usage with some performance trade-offs.