Code Llama 34B Instruct
Meta's 34B parameter code-specialized model with infilling capabilities and large context. Battle-tested base for enterprise fine-tuning.
The brutal truth
Historic milestone, but newer models like Qwen 3.6 27B and Qwen3-Coder beat it soundly while using fewer resources. Only use if tied to existing fine-tunes.
Files and Versions
Direct inspected tree artifacts from Hugging Face. Verified filenames, exact byte sizes, model weight shards, and configuration definitions.
Files and versions are hosted on Hugging Face. You can view all branches, commits, model weights, and tokenizer artifacts directly.
Inspect codellama/CodeLlama-34b-Instruct-hf /tree/main ↗Inspect /tree/main Repository ↗Decision matrix
✓ RUN / USE THIS IF…
- •You have legacy CodeLlama fine-tunes in production
- •You run dedicated 24GB enterprise hardware
- •Your 24GB GPU comfortably fits the model weights (~20GB VRAM) with dedicated headroom for inference.
- •You require 100% offline data privacy with zero third-party API logging, subscriptions, or prompt retention.
✕ WALK AWAY IF…
- •You are starting a fresh project in 2026 (use Qwen3-Coder)
- •Your GPU has less than 20GB VRAM and you cannot tolerate slow CPU/system RAM offload paging.
- •You prefer a plug-and-play web app interface and don't want to manage local model checkpoints, drivers, or runtimes.
- •You need specialized frontier reasoning that exceeds this model's parameter envelope.
True cost of ownership
About
Meta's 34B parameter code-specialized model with infilling capabilities and large context. Battle-tested base for enterprise fine-tuning.
What it does
Download the weights from Hugging Face, pick a quantisation that matches your GPU, and run it locally with Ollama, llama.cpp, vLLM, or a compatible runtime. Battle-tested enterprise code generation runs on your own hardware — no per-token bill, no third-party data handling.
Key features
Stable Python, Java, and Bash code generation
Battle-tested base model with hundreds of LoRA fine-tunes
Fits inside 24GB GPUs at 4-bit quantisation
Use cases
Inline edits, refactors, and test generation inside the editor.
Flag null-safety, security, and style issues before merge.
Turn long procedural functions into typed, testable modules.
Generate integration tests from route definitions and schemas.
Quantization & Hardware Fit
VRAM Hardware Compatibility & Memory Allocation
| Quant | VRAM | Quality / Fit on 24GB | Install / Run Command (OLLAMA) | |
|---|---|---|---|---|
| Q4_K_M● ACTIVE | 20 GB | Standard · ✓ Fits (4GB spare) | ollama run codellama:34b | |
| Q5_K_M | 24 GB | Standard · ✓ Fits (0GB spare) | ollama run codellama:34b | |
| Q8_0 | 36 GB | Standard · ✕ Needs 36GB | ollama run codellama:34b | |
| FP16 | 68 GB | Standard · ✕ Needs 68GB | ollama run codellama:34b |
Buyer doubts, cleared
Can I actually run Code Llama 34B Instruct on my 24GB GPU?
What's the fine print on the license?
Does the 100K tokens actually hold up in practice?
User reviews
The Q4_K_M build is the sweet spot. Anything larger needs serious hardware for marginal quality gains.
Used Code Llama 34B Instruct for a client project. Documentation was solid, though the fine-tuning path is less polished than base inference.
Reasoning quality is close to the bigger models for structured tasks. Open-ended planning still needs a frontier model.
Clean interface, consistent results once you land on a prompt you like. Wish there were more export presets for client handoffs.
