Llama Meta Released 2023-08 codecompletionfill-in-middle

Code Llama

Meta's code-specialized Llama. Foundation for many code tools. Now superseded by Qwen 2.5-Coder and Qwen 3-Coder for new work.

Best for code generation on Llama base
Sizes 7B · 13B · 34B · 70B
Context 16K
License Llama Community License
Min VRAM (default size, Q4) 5 GB
Rec VRAM 12 GB

What is Code Llama?

Code Llama is Meta's open-weights language model. Released in 2023-08, it ships in 4 sizes (7B, 13B, 34B, 70B) and is licensed Llama Community License. The most popular use case is code generation on Llama base.

VRAM and hardware

The smallest 7B size needs at least 5 GB VRAM; the largest 70B needs around 5 GB.

Size Min VRAM (Q4_K_M) Recommended VRAM Tokens/sec on 3090
7B 5 GB 8 GB ~25 tok/s
13B 9 GB 14 GB ~22 tok/s
34B 20 GB 30 GB ~17 tok/s
70B 42 GB 63 GB ~14 tok/s

How to run Code Llama locally

Option 1: Ollama (simplest)

ollama pull codellama
ollama run codellama

Option 2: Mullama (production)

mullama pull codellama
mullama run codellama

Option 3: llama.cpp (CLI)

# Download a GGUF from Hugging Face (search "codellama gguf")
./llama-cli -m codellama.Q4_K_M.gguf -p "Hello, AI!"

Option 4: Python with Mullama or llama-cpp-python

from mullama import Model, Context
model = Model.load("codellama.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256))

What you can build with Code Llama

  • code generation
  • legacy Llama workflows

Hardware it fits on

  • Apple Silicon (8GB): 7B in MLX
  • Apple Silicon (16GB): 13B in MLX
  • Apple Silicon (32GB): 34B in MLX or GGUF
  • Apple Silicon (64GB+): 70B in MLX
  • RTX 3090 (24GB): 34B at Q4_K_M
  • RTX 4090 (24GB): same as 3090, ~30% faster
  • RTX 5090 (32GB): 34B at Q8_0
  • 2× RTX 6000 Ada (96GB total): 70B at Q4_K_M

Related models in the Llama family

  • Llama 3.1 — 8B, 70B, 405B · general-purpose chat, agentic workflows, code
  • Llama 3.2 — 1B, 3B · low-end hardware, mobile, edge deployment
  • Llama 3.2 Vision — 11B, 90B · vision + chat on desktop GPUs
  • Llama 3.3 — 70B · best-in-class 70B for desktops

Other models you might consider

  • Qwen 2.5 (Alibaba) — 0.5B, strongest 7B-32B on consumer hardware
  • Qwen 2.5-Coder (Alibaba) — 0.5B, best open-weight code model in 2024-2025
  • Qwen 3 (Alibaba) — 0.6B, best all-round local LLM in 2025-2026
  • Qwen 3-Coder (Alibaba) — 30B, long-context code agent workflows
  • DeepSeek R1 (DeepSeek) — 1.5B, reasoning, math, step-by-step problem solving
  • DeepSeek V3 (DeepSeek) — 671B (37B active MoE), frontier-class open-weights LLM (needs data-center GPUs)