When should I choose BF16 over FP16?

BF16 is generally better for training on modern NVIDIA hardware (Ampere and newer) because it offers a larger dynamic range similar to FP32, which reduces the risk of overflows.

How do I calculate how much VRAM I need for a model?

You can estimate memory by multiplying the number of parameters (in billions) by the bytes per parameter (e.g., 0.5 bytes for 4-bit, 2 bytes for FP16, and 4 bytes for FP32).

Does quantization significantly degrade model performance?

While there is a slight trade-off, modern methods like 4-bit NormalFloat (NF4) preserve model reasoning and structure remarkably well, often showing nearly identical loss metrics to full precision models.

What is the benefit of using 4-bit quantization?

It reduces the memory footprint of a model by up to 8x compared to FP32, allowing large models to run on consumer-grade GPUs with minimal loss in reasoning quality.

Quantization & Model Optimization

Name: Quantization & Model Optimization
Author: atrawog

byatrawog

データサイエンスとML

Optimizes large language models for efficient inference and training using various precision types and memory estimation techniques.

概要

This skill provides specialized guidance for implementing model quantization strategies to reduce memory footprints and accelerate inference speeds. It covers a comprehensive range of precision types—from high-fidelity FP32 to resource-efficient 4-bit configurations—and includes practical code snippets for memory estimation, model size measurement, and advanced BitsAndBytes configurations. Ideal for developers working with memory-constrained GPUs, it also facilitates QLoRA setup to enable model fine-tuning on consumer-grade hardware while preserving model performance and reasoning capabilities.

主な機能

Precision management (FP32, FP16, BF16, INT8, INT4)
Advanced BitsAndBytes configuration for 4-bit and 8-bit loading
Automated memory estimation for LLM deployments
0 GitHub stars
Performance benchmarking and troubleshooting for VRAM optimization
QLoRA integration for resource-efficient fine-tuning

ユースケース

Fine-tuning large models using QLoRA on a single workstation
Running 7B+ parameter models on consumer GPUs with limited VRAM
Reducing cloud infrastructure costs by minimizing model memory requirements

概要

主な機能

Precision management (FP32, FP16, BF16, INT8, INT4)
Advanced BitsAndBytes configuration for 4-bit and 8-bit loading
Automated memory estimation for LLM deployments
0 GitHub stars
Performance benchmarking and troubleshooting for VRAM optimization
QLoRA integration for resource-efficient fine-tuning

ユースケース

Fine-tuning large models using QLoRA on a single workstation
Running 7B+ parameter models on consumer GPUs with limited VRAM
Reducing cloud infrastructure costs by minimizing model memory requirements