Ollama, LM Studio, and GPT4All Are All Just llama.cpp — Here's Why Performance Still Differs

Ollama, LM Studio, and GPT4All Are All Just llama.cpp — Here's Why Performance Still Differs When running local LLMs on an RTX 4060 8GB, the first decision isn't the model. It's the framework. llama.cpp, Ollama, LM Studio, vLLM, GPT4All — plenty of options. But under an 8GB VRAM constraint, the framework choice directly affects inference speed. A 0.5GB difference in overhead changes which models you can load at all. One extra API abstraction layer adds a few ms of latency. What follows is a comparison on identical hardware with identical models. Frameworks and Evaluation Criteria Framework Overview frameworks = { " llama.cpp (CLI) " : { " version " : " b8233 (2026-03) " , " backend " : " CUDA + Metal + CPU " , " quantization " : " GGUF (Q2_K ~ FP16) " , " API " : " CLI / llama-server (OpenAI-compatible) " , " strength " : " Minimal overhead, maximum control " , }, " Ollama " : { " version " : " 0.6.x " , " backend " : " llama.cpp (bundled) " , " quantization " : " GGUF (via Ollama Hub)