Local Inference

Unsloth Dynamic 3.0 GGUF quantization improves local LLM inference accuracy at matched size

Unsloth Dynamic GGUF 3.0: Faster Local Inference Without Sacrificing Accuracy

Unsloth Dynamic GGUF 3.0 is the latest iteration of Unsloth’s quantization format for running large models locally. It delivers more than 10% better top-1% accuracy at the same file size as every other GGUF provider, works with llama.cpp and Unsloth Desktop, and makes a 27B-parameter model runnable on as little as 7-8GB of RAM. This review explains what changed, how the quality claims are measured, and exactly which quant to pick for your hardware. ...

September 18, 2026 · 12 min · baeseokjae
Multi GPU local inference: drifting a large model across multiple GPUs for home inference

Multi GPU Local Inference: How to Drift a Model Across Your Home GPUs

Multi GPU local inference means spreading a single large language model across two or more GPUs in your home server so you can run models that exceed the VRAM of any one card, or serve them faster. You do this by “drifting” the model’s layers or tensors across your GPUs using either pipeline (layer) parallelism or tensor parallelism, configured through tools like llama.cpp and vLLM. The right strategy depends on your interconnect speed, your hardware mix, and whether your model architecture even supports the split. ...

September 1, 2026 · 9 min · baeseokjae