Best Open-Source LLMs You Can Run on 16 GB VRAM (As of 2026)

Running powerful open-source LLMs on 16 GB VRAM systems is feasible through quantization and optimized deployment. Converting models like Mistral Large 3 to 4-bit precision reduces VRAM usage by up to 4x, enabling execution on consumer-grade GPUs. Phi-3 Mini achieves 68.8 MMLU and 62.2 HumanEval scores at 3.8B parameters with 8 GB VRAM at 4-bit quantization, making it ideal for low-latency applications. Use vLLM with speculative execution to deploy Mixtral 8x7B on RTX 4090 via Docker for high-parameter workloads. Evaluate model size, quantization level, and inference tools like BitsAndBytes and Hugging Face Transformers to select the best fit for your VRAM and performance needs.

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