The Kaitchup – AI on a Budget

The Kaitchup – AI on a Budget

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Tutorials

Eagle 3 Speculators: When To Use Them?
Easier and faster speculative decoding, if you are in the right settings
 • Benjamin Marie
Accelerate Models with Quantization: Recipes for NVFP4, GPTQ, AWQ, SmoothQuant, AutoRound, and FP8
Focus on 4-bit and 8-bit quantization + vLLM benchmarking with accuracy and inference throughput
 • Benjamin Marie
Unsloth's Quantization-Aware Training (QAT) vs Post-Training Quantization (PTQ) for Small Models
Can a tiny LLM stay accurate under quantization thanks to QAT?
 • Benjamin Marie
Advanced LoRA Fine-Tuning: How to Pick LoRA, QLoRA, DoRA, PiSSA, OLoRA, EVA, and LoftQ for LLMs
A practical guide to parameter-efficient LLM adaptation on 16-bit and 4-bit models
 • Benjamin Marie
Generate Better Synthetic Datasets with a "User" LLM
User LLM + Qwen3 to generate fully synthetic dialogues
 • Benjamin Marie
Qwen3-VL Fine-Tuning on Your Computer
Model review, GPU requirements, and code explained step by step
 • Benjamin Marie
Choosing a GGUF Model: K-Quants, IQ Variants, and Legacy Formats
Reviewing the differences between each type and their impact on accuracy, throughput, and memory.
 • Benjamin Marie
Why Increasing Batch Size Doesn’t Always Speed Up Training
5 most common issues that decreases the batch training efficiency
 • Benjamin Marie
Serve Multiple LoRA Adapters with vLLM and Custom Chat Templates
Swap adapters per request, reuse your chat template, and run offline or via an OpenAI-compatible server.
 • Benjamin Marie
DenseMixer: Smarter MoE Routing That Doesn’t Break LoRA and QLoRA
Better MoE training for a slightly higher cost
 • Benjamin Marie
Gemma 3 270M: Can Tiny Models Learn New Tasks?
A case study with machine translation
 • Benjamin Marie
NVFP4: Same Accuracy with 2.3x Higher Throughput for 4-Bit LLMs
How to quantize LLMs with NVFP4
 • Benjamin Marie
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