SWE-bench-lite 61.2% — one of the highest scores for an open model on real GitHub issue resolution. Built-in native diff generation and test writing. Ideal for fully automated PR creation and code review pipelines.
This model requires a specialized High-VRAM environment. Ensure you have the latest CUDA Drivers or Metal Framework installed.
Minimum VRAM: 66GB VRAM Recommended
The DeepCoder-V2 model by Agentica is a 236B (MoE) parameter architecture optimized for code tasks. It requires approximately 64GB of VRAM to comfortably run locally using a Q4_K_M quantization. Extending the context window up to 131,072 tokens will dynamically allocate further VRAM, meaning high-bandwidth memory hardware is strictly advised.
For running DeepCoder-V2 at maximum tokens-per-second, we recommend using LM Studio or Ollama with a GGUF quantization (Q4_K_M or Q6_K). If you are multi-GPU, use vLLM to distribute the layers across your VRAM pool for optimal throughput.
You will need a GPU with at least 66GB of VRAM to run the Q4_K_M quantized version smoothly with a moderate context window.
The simplest method is utilizing Ollama by executing 'ollama run deepcoder-v2' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.