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 better than GPT-4 for code?
CodeLlama 34B is competitive with GPT-3.5-turbo on HumanEval but falls short of GPT-4 on complex multi-file reasoning. For generating boilerplate, single functions, and simple algorithms, 34B INT4 is excellent. For complex architectural decisions across large codebases, GPT-4 still leads.
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.
Related Tools
AI VRAM
Calculate the GPU VRAM required to run any LLM locally or on cloud GPU. Supports all quantization levels — FP32, FP16, INT8, INT4, and GGUF variants.
GPU Cloud Cost
Compare GPU cloud rental costs across RunPod, Lambda Labs, and Vast.ai. Calculate monthly spend for LLM inference, fine-tuning, and ML training workloads.
Llama 3 8B VRAM
How much GPU VRAM do you need to run Meta Llama 3 8B? At FP16 it needs 16 GB. At INT4/GGUF Q4 it fits in just 4–5 GB — runnable on consumer GPUs.
Related Comparisons
Related Guides
ai-gpu
How to Run LLMs on Kubernetes: GPU Setup Guide (2026)
A practical guide to deploying GPU nodes on Kubernetes, configuring the NVIDIA device plugin, sizing VRAM for LLM inference, and running vLLM or Ollama as a scalable serving stack.
ai-gpu
GPU Cloud Providers for AI/ML in 2026: RunPod, Vast.ai, Lambda Labs, and More
A practical comparison of GPU cloud providers for AI/ML workloads in 2026 — pricing, availability, setup complexity, and when to self-host instead.
ai-gpu
How Much VRAM Do You Need to Run LLMs? A Practical Guide
Calculate exactly how much GPU memory you need to run Llama 3, Mistral, Gemma, and other LLMs at different quantization levels. FP16, INT4, GGUF explained.