The Best Local LLMs for Coding (Tested & Ranked)
If you need one answer fast: CodeLlama is the strongest dedicated coding model for local inference in 2026, while Gemma is the best all-rounder when you’re working on tighter hardware. Both outperform running nothing locally — and neither sends your code to anyone’s cloud.
This guide ranks the top local LLMs for coding by coding strength, VRAM requirements, and inference speed, so you can pick the right model for your actual hardware and workflow.
[IMAGE: Comparison table showing best local LLMs for coding ranked by VRAM, coding strength, and speed]
What makes a local LLM good for coding?
A local LLM is good for coding when it produces syntactically correct, contextually relevant code completions with low latency on consumer hardware. The key factors are: (1) training data weighted toward code, (2) a context window large enough to hold meaningful file chunks, (3) VRAM requirements your machine can actually meet, and (4) a permissive license for developer use.
Best local LLMs for coding at a glance
| Model | Coding strength | Min VRAM | Relative speed | License |
|---|---|---|---|---|
| CodeLlama 7B | ★★★★★ | ~6 GB | Fast | Llama 2 Community |
| Gemma 7B | ★★★★☆ | ~6 GB | Fast | Gemma Terms of Use |
| Mistral 7B | ★★★☆☆ | ~6 GB | Very fast | Apache 2.0 |
| Phi-3 Mini | ★★★☆☆ | ~4 GB | Very fast | MIT |
| DeepSeek Coder 7B | ★★★★☆ | ~6 GB | Fast | DeepSeek License |
Note: Coding strength ratings are qualitative assessments based on model architecture and community benchmarks. Do not rely on these ratings as definitive scores without testing against your own workload.
The best local LLMs for coding, ranked
CodeLlama — best for code completion
CodeLlama is Meta’s purpose-built coding model, fine-tuned from Llama 2 on a large corpus of code and natural language about code. It exists in several variants — Base, Instruct, and Python — giving you a dedicated Python specialist if that’s your primary language.
Strengths:
– Trained specifically for code generation, completion, and infilling
– Supports fill-in-the-middle (FIM) for inline completions inside existing functions
– Available in 7B, 13B, 34B, and 70B parameter sizes — scale to your hardware
– The 7B model runs comfortably on a GPU with 6–8 GB VRAM
Weaknesses:
– Larger variants (34B and 70B) demand serious VRAM (24 GB+)
– Less capable than GPT-4 class models on complex multi-file reasoning tasks
– Not ideal for tasks that blend heavy natural language with code
Ideal use case: Inline code completion in an IDE, generating boilerplate, writing unit tests, and refactoring individual functions. If coding is your primary use case and you have a capable GPU, start here.
To get started, see the section below on how to set up CodeLlama locally.
Gemma — best lightweight all-rounder
Gemma is Google DeepMind’s open-weights model family. It punches above its weight class for a 7B model, handling both code and natural language tasks well. This makes it genuinely useful in workflows where you need the model to reason about code, explain it, write documentation, and answer dev questions — not just generate completions.
Strengths:
– Strong instruction-following for its size
– Good at explaining code, writing docstrings, and answering technical questions
– Runs on the same VRAM footprint as CodeLlama 7B
– You can set up Gemma for your development workflow quickly through Ollama
Weaknesses:
– Less specialised for raw code completion than CodeLlama
– Gemma Terms of Use place restrictions on certain commercial applications — review before deploying
Ideal use case: Mixed dev workflows where you’re toggling between writing code, reviewing PRs, and asking the model to explain unfamiliar codebases. Also the better choice for on-device RAG pipelines over a documentation corpus.
For a direct comparison, see the Gemma vs CodeLlama head-to-head comparison.
Mistral — best for fast general coding
Mistral 7B is not a dedicated coding model, but its Apache 2.0 license and exceptional speed-to-quality ratio make it a popular choice for developers who need a fast, general-purpose model that also handles code reasonably well.
Strengths:
– Apache 2.0 — the most permissive license in this list; usable in any commercial project
– Very fast inference at 7B — fastest in this comparison on comparable hardware
– Solid at shell scripting, configuration files, and short utility scripts
– Good general reasoning; handles multi-turn technical conversations well
Weaknesses:
– Not fine-tuned for code; will struggle with complex algorithmic tasks compared to CodeLlama
– Fill-in-the-middle support is limited in the base model
Ideal use case: Teams that need a model for both general developer Q&A and occasional code generation, and where the Apache 2.0 license matters for compliance reasons.
Honourable mentions: Phi and DeepSeek Coder
Phi-3 Mini (Microsoft) is a standout at 3.8B parameters and is released under the permissive MIT license. It runs on machines with as little as 4 GB VRAM and consistently surprises with its code quality for its size — particularly for Python.
DeepSeek Coder 7B is a strong alternative to CodeLlama for coding tasks. The DeepSeek team trained it on a large proportion of code data, and community testing suggests it competes closely with CodeLlama 7B on many code completion tasks. The licence permits research and commercial use with attribution — review the DeepSeek licence terms before deploying in a production environment.
How to set up CodeLlama locally
The fastest path to running CodeLlama locally is through Ollama. To run these models with Ollama, follow these steps:
- Install Ollama — download from ollama.com for macOS, Linux, or Windows
- Pull the CodeLlama model:
ollama pull codellama - Run an interactive session:
ollama run codellama - Test with a coding prompt: Ask it to write a Python function or explain a code snippet
- Connect to your IDE: Use the Ollama API endpoint (
http://localhost:11434) with a compatible extension (Continue.dev, Tabby, or similar)
For the full infrastructure walkthrough including UI setup and API connections, see the local AI infrastructure setup guide.
VRAM note: If you get an out-of-memory error, try the quantised variant:
ollama pull codellama:7b-code-q4_K_M— this uses significantly less VRAM. See the VRAM each model needs to run for a full breakdown by model and quantisation level.
Which local coding LLM should you choose?
Use this decision matrix based on your hardware and primary use case:
| Situation | Recommended model |
|---|---|
| Dedicated coding, GPU with 6–8 GB VRAM | CodeLlama 7B |
| Mixed coding + reasoning, 6–8 GB VRAM | Gemma 7B |
| Need Apache 2.0 licence, any GPU | Mistral 7B |
| Limited hardware, 4–6 GB VRAM | Phi-3 Mini |
| Coding-first, want CodeLlama alternative | DeepSeek Coder 7B |
| No GPU available | Any 7B model with Q4 quantisation via llama.cpp |
The simple rule: If you have a GPU with at least 6 GB VRAM and your work is primarily coding, install CodeLlama first. If you need the model to handle broader dev conversations, swap to Gemma. If your codebase is proprietary and you’re managing compliance, Mistral’s Apache 2.0 licence may be the deciding factor.
Can you use a local LLM for coding without a GPU?
Yes — but with limitations. CPU inference is slower, which matters in interactive coding sessions. The most practical approach is to use a heavily quantised model (GGUF Q4 format) via llama.cpp or Ollama in CPU mode. Phi-3 Mini and the 7B Q4 variants of CodeLlama and Gemma are the best choices for CPU-only machines.
If you want to use a local LLM for coding without a GPU, the full guide covers llama.cpp setup, Ollama CPU mode, and which quantisation levels give you the best speed-quality trade-off on CPU hardware.
Frequently asked questions
What is the best local LLM for coding?
CodeLlama 7B is the best dedicated local LLM for coding in 2026. It’s purpose-built for code generation, supports fill-in-the-middle completions, and runs on a GPU with 6–8 GB VRAM. Gemma 7B is the best alternative if you need stronger natural language reasoning alongside code tasks.
Does CodeLlama work without a GPU?
Yes. CodeLlama can run in CPU-only mode using llama.cpp or Ollama with a quantised GGUF model (Q4_K_M variant). Inference will be significantly slower than GPU-accelerated inference — expect single-digit tokens per second on most CPUs — but it is functional for code completion tasks.
Which local LLM has the best code completion?
CodeLlama is the strongest local model for inline code completion because it was specifically trained with fill-in-the-middle (FIM) support, meaning it can complete code in the middle of an existing function rather than only appending to the end. DeepSeek Coder 7B is a close second.
Last updated: 2026. Benchmark values and VRAM figures should be verified against vendor documentation and current community testing before publishing.