Exploring LLM Optimization: Qwen3-0.6B INT8 Quantization

Dhivakar G — Fri, 30 Oct 2026 08:00:42 +0000

Large Language Models are powerful, but their memory and computational requirements can make deployment challenging.

As part of my exploration of LLM optimization, I successfully worked on quantizing Qwen3-0.6B from FP32 to INT8 using Optimum-Quanto and PyTorch.

🔹 Base Model: Qwen/Qwen3-0.6B
🔹 Quantization: INT8 Weight Quantization
🔹 Framework: PyTorch & Optimum-Quanto
🔹 Model Integration: Hugging Face Transformers
🔹 Model Hosting: Hugging Face Hub
🔹 Inference: Google Colab with CUDA GPU

Key Implementations:

Applied INT8 weight quantization.
Saved and restored quantized model checkpoints.
Published the quantized model on Hugging Face.
Prepared a GPU-based inference workflow.

🤗 Hugging Face Model:
https://huggingface.co/dhivakarg/qwen3-0.6b-int8-quanto

This project strengthened my practical understanding of LLM optimization, model quantization, and efficient inference workflows.

Give it a try and share your feedback! I'd love to hear your thoughts.

NCM Architecture in LLMs: Rethinking How AI Generates Text

Dhivakar G — Thu, 29 Oct 2026 14:45:17 +0000

Traditional LLMs generate text through sequential next-token prediction. But what if we could rethink how AI generates text?

Next-Concept Modeling (NCM) explores an alternative approach, generating hierarchical representations from coarse to fine while enabling parallel predictions within each level.

In my latest blog, I explore the core architecture, its generation workflow, potential performance benefits, and the challenges of moving beyond conventional token-by-token generation.

📖 Read the full article for a deeper technical overview:

https://dhivs25.medium.com/ncm-architecture-in-llms-rethinking-how-ai-generates-4d13363d4dbb

I’d love to hear your thoughts: Could hierarchical, parallel generation play an important role in the future of LLM inference?

DEV Community: Dhivakar G

Exploring LLM Optimization: Qwen3-0.6B INT8 Quantization

NCM Architecture in LLMs: Rethinking How AI Generates Text