Five open-source coding models now run on consumer hardware like M2 MacBooks and RTX 4060 GPUs, rivaling performance of paid services.

Google DeepMind's Gemma 4 E4B-IT (4.5 billion effective parameters) handles text, images, and audio natively with a 128K context window, runs on 6–8GB VRAM, though its Codeforces ELO of 940 makes it weaker at pure coding than other models on this list.

OpenAI released gpt-oss-20B with Apache 2.0 license; despite the 20B label it uses a MoE (mixture-of-experts) architecture with only 3.6B active parameters, fits in 16GB memory, and scores Codeforces ELO 2516 with tools—ahead of o3-mini's 2073.

DeepSeek-R1-Distill-Llama-8B distills reasoning patterns from DeepSeek's 671B model into an 8B base, self-verifies and generates chain-of-thought answers, scores 39.6 on LiveCodeBench and 1205 Codeforces rating, runs on 8GB VRAM with MIT license.

Qwen3.6-35B-A3B uses 3B active parameters (35B total on disk) and achieves 73.4 on SWE-bench Verified—testing real GitHub issue resolution in actual codebases—but requires 20GB+ with Q4 quantization, targeting M2 Pro 32GB or 24GB GPUs.