Best Local LLM for Coding in 2026
This guide started as an AI draft and then a human pulled apart every benchmark claim before it shipped. If a model is named, the eval is cited.
Updated May 2026 · Covers Qwen3, Phi-4, Codestral, DeepSeek-Coder-V2, Llama 3.3
The best local LLM for coding depends on your GPU. For 8 GB GPUs: Qwen3 8B. For 12 GB: Qwen3 14B or Phi-4 14B. For 16 GB: Codestral 22B (autocomplete) or DeepSeek-Coder-V2 16B. For 24 GB: Qwen3 32B. For 48 GB+: Llama 3.3 70B. This guide covers every tier with VRAM requirements, Ollama setup, tool integration, and a use-case matrix.
Not sure what your hardware can run? Use the VRAM Calculator, or read the best local LLMs guide for general-purpose picks.
TL;DR
- 8 GB GPU: Qwen3 8B — runs at 35-45 tok/s, handles completions and code review
- 12 GB GPU: Qwen3 14B for general coding, Phi-4 14B if you want near-70B benchmark quality
- 16 GB GPU: Codestral 22B for IDE autocomplete, DeepSeek-Coder-V2 16B for code generation
- 24 GB GPU: Qwen3 32B — best single-GPU coding model available
- 48 GB+ VRAM: Llama 3.3 70B — GPT-4 competitive on coding benchmarks
Best Coding LLM Per GPU Tier
Buy RTX 4070 on Amazon| VRAM | Best Model | Quant | VRAM Used | Speed | Example GPU |
|---|---|---|---|---|---|
| 8 GB | Qwen3 8B | Q4_K_M | ~5 GB | 35-45 tok/s | RTX 4060 |
| 12 GB | Qwen3 14B | Q4_K_M | ~9 GB | 25-35 tok/s | RTX 4070 |
| 12 GB | Phi-4 14B | Q4_K_M | ~9 GB | 25-35 tok/s | RTX 4070 |
| 16 GB | Codestral 22B | Q4_K_M | ~14 GB | 20-30 tok/s | RTX 4060 Ti 16GB |
| 16 GB | DeepSeek-Coder-V2 16B | Q4_K_M | ~10 GB | 25-35 tok/s | RTX 4060 Ti 16GB |
| 24 GB | Qwen3 32B | Q4_K_M | ~20 GB | 25-35 tok/s | RTX 4090 |
| 48 GB+ | Llama 3.3 70B | Q4_K_M | ~40 GB | 14-20 tok/s | Dual 3090 / A6000 |
VRAM figures are approximate at Q4_K_M quantization including KV cache headroom. Use the VRAM Calculator for exact sizes.
Model Details and Recommendations
Qwen3 8B
8 GB GPU ~5 GB Q4_K_M 35-45 tok/s on RTX 4060The best coding model for 8 GB GPUs. Surprisingly capable for completions and short code review tasks.
Strengths
- + Python, JS, TypeScript completions
- + Instruction-following quality above its size class
- + Fast enough for interactive autocomplete
Limitations
- - Context window limits on large codebases
- - Not ideal for complex multi-file refactors
Qwen3 14B
12-16 GB GPU ~9 GB Q4_K_M 30-40 tok/s on RTX 4060 Ti 16GBThe sweet spot for most developers. Runs on a 12 GB GPU and handles the full coding workflow — autocomplete, chat, review, and debugging.
Strengths
- + Strong code generation across 20+ languages
- + Good at code review and explanation
- + Handles moderate-length context well
Limitations
- - Slower than 8B for autocomplete latency
Phi-4 14B
12-16 GB GPU ~9 GB Q4_K_M 30-40 tok/s on RTX 4060 Ti 16GBBest 14B model if you prioritize reasoning quality. Near-70B benchmark performance at 12 GB VRAM.
Strengths
- + Benchmark quality close to 70B models
- + Excellent on Python and data science tasks
- + Strong logical reasoning for debugging
Limitations
- - Less code-specialized than Codestral
- - Slightly lower multilingual code support
Codestral 22B
16 GB GPU ~14 GB Q4_K_M 20-30 tok/s on RTX 4060 Ti 16GBMistral's dedicated coding model. The best choice if IDE autocomplete is your primary use case.
Strengths
- + Best fill-in-the-middle (FIM) autocomplete
- + Code-specialized training across 80+ languages
- + Excellent for IDE integration via Continue.dev
Limitations
- - Less capable for general chat vs Qwen3
- - Needs 16 GB to run comfortably
DeepSeek-Coder-V2 16B
16 GB GPU ~10 GB Q4_K_M 25-35 tok/s on RTX 4060 Ti 16GBA solid code-specialized model that fits on 16 GB. Good alternative to Codestral if you want stronger algorithmic reasoning.
Strengths
- + Strong on Python, C++, and systems code
- + Good at algorithmic problems
- + Well-tested for code generation benchmarks
Limitations
- - Less instruction-tuned than Qwen3 for general tasks
Qwen3 32B
24 GB GPU ~20 GB Q4_K_M 25-35 tok/s on RTX 4090The best coding model you can run on a single consumer GPU. If you have an RTX 4090 or equivalent, this is the pick.
Strengths
- + Best single-GPU coding model in 2026
- + Handles large codebases and long context well
- + Excellent at complex refactors and agentic tasks
- + Strong reasoning for hard debugging problems
Limitations
- - Needs a 24 GB GPU — RTX 4090 or equivalent
Llama 3.3 70B
48 GB+ VRAM ~40 GB Q4_K_M 14-20 tok/s on dual 3090 / A6000The best open-weights coding model period, but requires serious hardware. Runs well on Mac Studio M4 Max (128 GB unified memory) or multi-GPU setups.
Strengths
- + Competitive with GPT-4 on coding benchmarks
- + Best open-weights model for complex agentic coding
- + Excellent at code review and architecture discussions
Limitations
- - Requires 48 GB+ of total VRAM
- - Slow on consumer hardware without NVLink
Use-Case Matrix
How each model performs across common coding tasks. Ratings are relative to the model's size tier — not absolute comparisons to cloud APIs.
| Use Case | Qwen3 8B | Qwen3 14B | Codestral 22B | DSC-V2 16B | Phi-4 14B | Llama 3.3 70B |
|---|---|---|---|---|---|---|
| Autocomplete (FIM) | Good | Very Good | Excellent | Very Good | Good | Very Good |
| Chat / Q&A | Very Good | Excellent | Good | Very Good | Excellent | Excellent |
| Code Review | Good | Very Good | Good | Very Good | Very Good | Excellent |
| Agentic (multi-file) | Fair | Good | Fair | Good | Good | Excellent |
| Debugging | Good | Very Good | Good | Very Good | Very Good | Excellent |
| Refactoring | Good | Very Good | Good | Very Good | Very Good | Excellent |
Ollama Commands
Install Ollama from ollama.com. Each command below downloads and runs the model automatically on first use.
ollama run qwen3:8b 8 GB Best 8 GB coding pick ollama run qwen3:14b 12 GB Best 12 GB all-round coder ollama run phi4 12 GB Near-70B benchmark quality at 14B ollama run codestral 16 GB Best for IDE autocomplete (FIM) ollama run deepseek-coder-v2:16b 16 GB Strong general code generation ollama run qwen3:32b 24 GB Best single-GPU coding model ollama run llama3.3:70b 48 GB Best open-weights, needs 48 GB+ Once Ollama is running, it exposes an OpenAI-compatible API at http://localhost:11434/v1 — use this URL in any IDE extension or coding tool.
IDE and Tool Integration
These tools connect to your local Ollama instance to bring coding assistance into your editor — no cloud API required.
Continue.dev
VS Code and JetBrains extension that connects to Ollama for inline autocomplete and chat. The closest open-source GitHub Copilot replacement. Supports FIM autocomplete with Codestral.
Best models: Codestral 22B (autocomplete), Qwen3 14B (chat)
Setup: Install Continue extension → set provider to Ollama → select model.
Aider
Terminal-based agentic coding tool. Reads your codebase, makes multi-file edits, and commits changes. Works with Ollama via the OpenAI-compatible API endpoint.
Best models: Qwen3 32B or Llama 3.3 70B for complex agentic tasks
Setup: Run: OLLAMA_API_BASE=http://localhost:11434/v1 aider --model ollama/qwen3:32b
Cursor (local mode)
Cursor supports custom OpenAI-compatible endpoints. Point it at your Ollama server to use local models for chat and inline edits while keeping your code off cloud servers.
Best models: Qwen3 32B or DeepSeek-Coder-V2 16B
Setup: Cursor Settings → Models → Add Model → set base URL to http://localhost:11434/v1
VS Code + Ollama API
Any VS Code extension that supports OpenAI-compatible APIs (CodeGPT, Codeium local, etc.) can connect to Ollama running locally. No cloud dependency, no API key required.
Best models: Qwen3 14B for chat, Codestral for autocomplete
Setup: Set API base to http://localhost:11434/v1 and API key to "ollama" (any string).
Also Worth Knowing: Granite 3 8B
IBM's Granite 3 8B is a code-focused model designed for enterprise use cases. At Q4_K_M it fits in 8 GB VRAM and is notably strong on SQL, API generation, and structured output tasks. It is licensed under Apache 2.0, which makes it suitable for commercial deployment without restrictions. Run it with ollama run granite3:8b. For most developers Qwen3 8B is the better general-purpose pick, but Granite 3 is worth trying if you work heavily with databases or enterprise APIs.
Frequently Asked Questions
What is the best local LLM for coding in 2026?
The best local LLM for coding depends on your GPU. For 8 GB GPUs, Qwen3 8B at Q4_K_M is the top pick — it handles completions and code review well at 35-45 tokens/sec on an RTX 4060. For 12 GB GPUs, Qwen3 14B or Phi-4 14B are both excellent. For 24 GB GPUs, Qwen3 32B is the best all-around coding model available locally. If you have 48 GB or more, Llama 3.3 70B is competitive with GPT-4 on coding tasks.
Can I use a local LLM as a GitHub Copilot replacement?
Yes. Tools like Continue.dev (VS Code/JetBrains extension) connect to Ollama and provide inline autocomplete and chat — functionally similar to GitHub Copilot but entirely local. Qwen3 14B or Codestral 22B are the best models for this use case. Aider also works with Ollama for agentic coding workflows. The main tradeoff is speed: a local model on a 12-16 GB GPU produces autocomplete suggestions in 1-3 seconds vs near-instant for cloud APIs.
What VRAM do I need for a coding LLM?
For useful coding assistance, 8 GB VRAM is the minimum — it runs Qwen3 8B at Q4_K_M. For the best balance of quality and speed, 12-16 GB VRAM is the sweet spot, running Qwen3 14B, Phi-4 14B, or DeepSeek-Coder-V2 16B. At 24 GB you can run Qwen3 32B, which is strong enough for complex refactors and agentic workflows. 48 GB or more lets you run Llama 3.3 70B, the most capable open-weights coding model.
Is Codestral better than Qwen3 for coding?
Codestral 22B is Mistral's code-specialized model and is particularly strong on fill-in-the-middle (FIM) autocomplete tasks, which makes it excellent for IDE integration. Qwen3 14B and 32B are stronger general-purpose coding assistants and better at code review, explanation, and complex reasoning. For pure autocomplete in an IDE, Codestral is a strong choice. For chat-based coding help and code review, Qwen3 32B is the better pick.
How do I connect a local LLM to VS Code for coding?
Install Ollama and pull your chosen model (e.g. "ollama run qwen3:14b"). Then install the Continue.dev extension in VS Code. In Continue's settings, set the provider to Ollama and select your model. This gives you inline autocomplete and a chat panel — similar to GitHub Copilot but running entirely on your machine. For agentic coding (multi-file edits), use Aider with the Ollama backend instead.
Can DeepSeek-Coder-V2 run on a 16 GB GPU?
Yes. The DeepSeek-Coder-V2 16B distill at Q4_K_M requires approximately 10 GB of VRAM, which fits comfortably on a 16 GB GPU (RTX 4060 Ti 16GB, RTX 4080, etc.). It delivers strong code generation quality, especially for Python, TypeScript, and systems languages. Run it with "ollama run deepseek-coder-v2:16b".
What is the best coding LLM for CPU-only (no GPU)?
For CPU-only setups, Qwen3 8B at Q4_K_M is the best coding model you can realistically use — it needs about 5 GB of RAM beyond your OS and runs at roughly 5-8 tokens per second on a modern CPU. That is slow but functional for asking questions and reviewing code. If you are doing coding work frequently, even an 8 GB GPU like the RTX 4060 will provide a dramatically better experience.
Related Guides
Know which model you want? Check exact VRAM requirements or find the right GPU.
Sources & methodology
Model parameter counts, context lengths and the VRAM estimates above come from a mix of official model cards and open benchmarks. The full sitewide methodology is documented on the methodology page. The three sources that did most of the work for this guide:
- Hugging Face Hub. Parameter counts, context lengths and tokenizer details for every coding model recommended.
- Ollama. The runtime that ships pre-packaged GGUFs of Qwen2.5-Coder, DeepSeek-Coder and Codestral.
- Modal: How much VRAM do I need for LLM inference. VRAM estimates used to match each coding model to a hardware tier.
Spot a number that does not match the linked source? Email [email protected] and I will update the guide.