GGUF memory guide: Ollama, LM Studio and llama.cpp

This local LLM guide compares Ollama, LM Studio, llama.cpp and Apple's MLX while explaining why download size alone cannot tell you whether a model will fit. The speaker separates model weights, KV cache and working memory, then applies that breakdown to a laptop with 6GB of VRAM and 16GB of RAM, a 24GB GPU desktop and a Mac with 64GB of unified memory. The hardware examples are estimates, not benchmarks.

The GGUF section explains how weights and metadata share one file, compares Q4_K and Q6_K precision, and links file format to engine compatibility. Smaller quants save memory; answer quality still needs testing.

Qwen 3 30B and 32B illustrate the difference between mixture-of-experts and dense models: similar storage requirements can mean different amounts of computation per token. The guide also distinguishes the wait for the first token from subsequent generation speed. Context length, CPU offloading and quantization affect these costs in different ways.

For viewers who want to run models locally, the software comparison covers Ollama's defaults, LM Studio's memory estimator, llama.cpp's controls and MLX's Apple Silicon support. The speaker notes conflicting Ollama documentation about context defaults and recommends checking the loaded context and CPU/GPU split with ollama ps.

Later sections discuss prompt caching, speculative decoding and ways to exceed VRAM capacity. FreeToken's performance figures are attributed to its research paper and described as independently unverified. The specialist DeepSeek V4.1 Flash SSD setup requires antirez's dwarf star engine; the cited page supplies no measured speed. The closing advice is to estimate memory, time real prompts and check answer quality.