gguf

5 articles · newest first

Illustration of compressing a large block of AI experts into a smaller box

Llama 4 Quantized: GGUF Sizes & Memory, Explained Honestly

Quantization is the only reason Scout runs at home — but MoE means you shrink all 109B, not the 17B active. The honest GGUF memory ladder, the Q4_K_M sweet spot, and how low you can safely go.

Aliteq Team · Sep 22 · 8 min

Flat illustration of a person compressing a large cube into a small box, on a teal background

How to Quantize a Local LLM in 2026 (GGUF Q4 vs Q6 vs Q8 — and What You Lose)

Quantization is the trick that lets a 16GB card run models it has no business running. Here's how GGUF quant levels work, which one I default to, the memory math, and the honest quality trade — so you get the most model your VRAM can hold.

Aliteq Team · Sep 22 · 9 min

Rows of GPUs mounted in an open server rack used for large-scale AI inference

DeepSeek's 'cheap' AI model costs 3 cents a query in the cloud. running it yourself needs $10k of GPUs

the model that made headlines for being nearly free to rent turns out to be one of the most expensive things you could try to self-host

Aliteq Team · Aug 4 · 6 min

Code representing a local AI inference engine

vLLM, Ollama or llama.cpp? the three ways to run local AI, and which one is actually for you

They all run local models, but they're built for completely different people. One's for easy desktop use, one's for flexibility, one's for serving at scale. Here's how to pick without wasting a weekend.

Aliteq Team · Jul 28 · 9 min

The ExLlamaV2 GitHub repository page showing its MIT license and description

is ExLlama free? yes — and it's the fastest way to run a local model, with one big condition

ExLlamaV2 is open-source and free (MIT), and on a GPU it's roughly twice as fast as llama.cpp. The catch: your model has to fit entirely in VRAM. Here's exactly when to use each one.

Aliteq Team · Jul 27 · 8 min