clevis@tinyweights:/home$ cat ./welcome.txt
> Small language models — Gemma, Phi, SmolLM, Qwen, and the rest — are getting surprisingly capable. Benchmarks, local deployment, quantization, and hands-on guides for running small LLMs on real hardware.
> Benchmarks, deployment guides, and hands-on for devs running AI on real hardware.
23 posts · last updated 2026-05-25 · all writing CC BY 4.0
clevis@tinyweights:/home$ ls -lh --sort=time
05-25
9min
#small-models
How to Run a Small LLM in Your Browser with WebLLM (No Install, No API)
05-25
6min
#small-models
How to Run Ministral 3 Locally: Mistral's 3B, 8B, and 14B Vision Models
05-24
7min
#small-models
MedGemma 1.5: Google's 4B Medical Vision-Language Model You Can Run Locally
05-24
6min
#small-models
Qwen3.5-4B vs Phi-4-mini: Choosing the Right 4B Model for Local Inference
05-23
11min
#small-models
Running Local LLMs on Low-VRAM Windows GPUs (6GB and 8GB Cards) in 2026
05-21
10min
#small-models
What Can You Actually Do With a Local Small LLM? A Practical Guide
05-20
8min
#small-models
Running LLMs on Raspberry Pi 5: A Practical Guide with Real Benchmarks
05-19
7min
#small-models
The Complete Guide to Running Small LLMs on Apple Silicon (2026)
05-18
6min
#small-models
How to Run Phi-4-mini Locally: Microsoft's 3.8B Model with 128K Context
05-17
8min
#small-models
How Much RAM Do You Actually Need to Run Local LLMs?