ai-gpu
CodeLlama 34B VRAM Requirements
GPU VRAM requirements for CodeLlama 34B — Meta's largest code-specialized LLM. At INT4 it needs ~17 GB, fitting on an RTX 4090 or A10G for local code generation.
CodeLlama 34B: The Open-Source Coding Model
CodeLlama is Meta's code-specialized family built on Llama 2. The 34B variant offers the best code quality while remaining runnable on a single high-end consumer GPU.
VRAM by Quantization
| Quantization | VRAM | Minimum GPU |
|---|---|---|
| FP16 | ~68 GB | A100 80GB |
| INT8 | ~34 GB | A100 40GB |
| INT4 / GGUF Q4 | ~17 GB | RTX 4090 24GB |
| GGUF Q8_0 | ~34 GB | A100 40GB |
CodeLlama Family
| Size | INT4 VRAM | Best for |
|---|---|---|
| 7B | ~4 GB | Fast autocomplete, small scripts |
| 13B | ~7 GB | Standard coding tasks |
| 34B | ~17 GB | Complex multi-function code |
| 70B | ~38 GB | Near-GPT-4 code quality |
Context Length Advantage
CodeLlama's 100K context window lets you feed entire Python modules or TypeScript projects into the model. This is transformative for refactoring and understanding large codebases. Most other 34B models max out at 4K–8K context.
Recommended Stack
For local coding assistant use:
- ›Model: CodeLlama-Instruct-34B GGUF Q4_K_M
- ›Runtime: Ollama or llama.cpp
- ›IDE: Continue.dev plugin (VS Code/JetBrains)
- ›GPU: RTX 4090 or A10G
Key Terms
Full glossary →VRAM (Video RAM)
Memory on a GPU used to store model weights, activations, and KV cache during LLM inference. VRAM is the primary constraint when running large language models locally.
Quantization
A technique to reduce model memory usage by representing weights in lower precision (INT8, INT4, GGUF-Q4). Quantization trades a small accuracy loss for significant VRAM reduction.
Frequently Asked Questions
What GPU runs CodeLlama 34B?
At GGUF Q4_K_M (~17 GB), CodeLlama 34B fits on an RTX 4090 (24GB) or A10G (24GB). For production serving with longer context (CodeLlama supports 100K tokens), an A100 40GB gives more headroom for the KV cache.
Is CodeLlama 34B still competitive for code generation?
CodeLlama 34B (built on Llama 2, released 2023) was strong for its time on HumanEval but has since been surpassed by newer open-weight coding models (Qwen Coder, DeepSeek-Coder, and newer Llama/CodeLlama generations). For generating boilerplate, single functions, and simple algorithms, 34B INT4 is still usable and cheap to run — but check current-generation coding models before committing to it for new production use.
What CodeLlama variants exist?
Meta released 3 variants: CodeLlama (base), CodeLlama-Python (Python-optimized), and CodeLlama-Instruct (instruction-following). Each is available in 7B, 13B, 34B, and 70B sizes. For local coding assistance, CodeLlama-Instruct 34B GGUF Q4 is the recommended choice.
How does long context affect VRAM in CodeLlama?
CodeLlama's 100K context window is its standout feature for code — it can process entire codebases. But at 16K context, the KV cache adds ~3 GB VRAM for the 34B model. At 100K context, the KV cache grows to ~20 GB — you'd need an A100 80GB for INT4 + long context.
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