If I cancel Codex today whats the next best local inference agent?
High agreement — the answer is well-supported across models.
Models agree on
- ✓StarCoder 2 is a top recommendation for local inference due to its balance of performance, open-source licensing, and manageable hardware requirements.
- ✓Code Llama is another strong contender, recognized for its code generation capabilities but with licensing considerations for commercial use.
- ✓The transition from Codex to local inference offers benefits such as enhanced privacy, cost predictability, and the ability to customize models.
- ✓Models' hardware requirements, especially VRAM, are critical factors in selecting a local inference agent.
Points of disagreement
- ~While both models point to Code Llama, one explicitly states its license is 'Meta-research (non-commercial use)' and requires a separate agreement for commercial deployment, whereas the other generally lists 'Phi (Meta AI)' as being 'aimed at providing safe and open AI for research and commercial use' without highlighting the restrictive commercial terms for Code Llama itself. This divergence highlights a different emphasis on the commercial viability/ease of licensing for Code Llama models.
- ~One model provided detailed quick-start guides with specific code snippets for both GPU and CPU deployment, while the other offered higher-level descriptions of models without concrete deployment instructions.
If you're looking to transition from Codex to a local inference agent today, you have several robust open-source and locally deployable options, with varying trade-offs in performance, hardware requirements, and licensing.
Top Contenders for Local Inferencing
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StarCoder 2 (7B/15B):
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Overview: This model by BigCode is highlighted as a strong contender due to its balance of accuracy, open-source licensing (Apache 2.0), and manageable hardware requirements. It's trained on 2 TB of public code up to 2023, offering broad language coverage.
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Strengths: Superior performance on benchmarks like HumanEval and MBPP compared to Code Llama (e.g., +12% pass@1 over Code Llama-7B). It's highly quantization-friendly, with the 7B variant running on an RTX 3060 (12 GB) at ~30 tokens/s. Excellent for commercial use due to its permissive license.
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Weaknesses: Lacks an official "Copilot-style" plugin, requiring local LSP or Tabnine-local integration. Can be slightly slower on very large contexts.
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Quick-start (GPU via vLLM): bash pip install "vllm[flash-attn]" git lfs install git clone https://huggingface.co/bigcode/starcoder2-7b python -m vllm.entrypoints.openai.api_server
--model bigcode/starcoder2-7b
--tensor-parallel-size 1
--port 8000 -
Quick-start (CPU via llama.cpp): bash git clone https://github.com/ggerganov/llama.cpp cd llama.cpp make ./quantize -m starcoder2-7b.gguf -f q4_k_m ./main -m starcoder2-7b.gguf -c 4096 -b 512 -ngl 8
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Code Llama (7B/13B/34B):
- Overview: Developed by Meta, Code Llama is praised for its "creativity" in code generation. It comes in various sizes, with larger models offering enhanced capabilities but demanding more VRAM.
- Strengths: Excels at generating novel algorithms and has extensive documentation with example prompts. Benefits from a strong community.
- Weaknesses: Its Meta-research license limits commercial deployment without a separate agreement, making StarCoder 2 a safer commercial choice. Larger variants (13B/34B) need high-end GPUs.
- Quick-start (CPU via llama.cpp, 7B):
bash
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
python convert_hf_to_gguf.py
--model code-llama/CodeLlama-7b-Instruct-hf
--outfile code-llama-7b.gguf ./main -m code-llama-7b.gguf -c 4096 -b 512 -ngl 8
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Other Viable Options:
- DeepSeek-Coder Base (7B): Apache 2.0 licensed, strong for general-purpose coding, particularly Python.
- Mistral-Codex (7B): Apache 2.0 licensed, good for C/C++ and low-latency completions.
- GPT-4-All-J (6B): MIT licensed, suitable for very low-resource CPUs, but with lower accuracy.
- Tabnine-Local (8B): A proprietary, commercial (free tier) option offering plug-and-play IDE integration with a closed model.
Why Transition from Codex?
Cancelling Codex means losing the cloud-only convenience of GitHub Copilot. However, you gain significant advantages:
- Privacy: 100% offline operation, ensuring your code stays local.
- Cost Predictability: No recurring fees or pay-per-token charges.
- Customization: Ability to LoRA-tune models on your own repositories.
- Licensing Flexibility: Open-source models (like StarCoder 2) offer free commercial use, unlike Codex's commercial-only license.
Key Considerations for Your Choice
- Hardware: Your GPU VRAM (ideally ≥ 12 GB for optimal performance) heavily influences model choice. CPU-only setups benefit greatly from quantized (GGUF) models with tools like
llama.cpp. - Licensing: Crucial for commercial projects. Apache 2.0 licensed models like StarCoder 2 are generally ideal.
- Performance vs. "Creativity": StarCoder 2 offers strong benchmark performance, while Code Llama excels in generating novel code.
- IDE Integration: Look for models and tools that offer good integration, such as through OpenAI-compatible endpoints, LSP-based plugins, or tools like Tabnine-local.
Ultimately, the "best" agent depends on your specific needs: commercial viability, hardware constraints, and the balance between raw performance and creative code generation.
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