Introducing a new Triton kernel technology that significantly boosts inference speed by improving memory access patterns during NF4 quantization, the process of reducing AI model size.
Imagine this: What if the AI chatbot you use daily could provide answers much faster and more smoothly than before? If the time we spend inputting questions and waiting for answers were reduced, conversations would become even more natural. Recently, an exciting technical breakthrough for this ‘speed boost’ has been made in the field of AI technology. A new way to process ‘quantization’—a technique that reduces the weight of the massive AI models we use—has emerged, and it is significantly faster.
Why does this matter?
The large language models (LLMs) we commonly encounter have billions of parameters (numerical values that store information within the model). To run these models on consumer computers or smartphones, the size of the model must be drastically reduced, making ‘quantization’ (a technique that reduces data size by lowering numerical precision) essential. Among them, NF4 (NormalFloat 4-bit), a 4-bit quantization technique, is a very clever technology that significantly reduces model size while minimizing performance degradation.
However, there was one problem. Even if the model became smaller, if ‘dequantization’ (the process of unpacking compressed data back into an operable state) was slow, the overall AI performance would suffer from a bottleneck. It’s like loading a large truck with cargo, only to have the delivery delayed because the unloading process is slow. The newly developed optimization technology makes this process much faster, contributing significantly to shortening the total time it takes for an AI to produce an answer. Source 11
Simple explanation: You won’t get lost if you have a map
AI model parameters are like books filling a massive library. We compress these books and store them in a warehouse (memory), but for an AI to create an answer, it must decompress these books to read their contents.
The existing ‘bitsandbytes’ (a representative library for performing quantization operations) method checked for complex ‘forks’ (branching operations) one by one whenever it needed to find content, and then decompressed it. It was similar to wandering through a maze-like path in a library to find a book, checking every path one by one.
On the other hand, the new kernel technology based on Triton (a programming tool that helps with low-level GPU optimization in the PyTorch environment) takes a completely different approach. Source 10
This method pre-copies a ‘lookup table’ (a map showing where data is located) and spreads it out in ‘shared memory,’ which has the fastest access. Source 3 By doing this, it’s like holding a library map in your hand, allowing you to find the desired data immediately without wandering through a maze. Since shared memory is 12–15 times faster than standard global memory, the overall data processing speed increases dramatically. Source 3
Current status
The effectiveness of this new technology has already been clearly proven with numbers. The research team applied this technology to large language models such as Gemma 27B, Qwen3 32B, and Llama3.3 70B. As a result, the speed of the kernel itself, which is the core of the operation, became up to 2.2x faster, and the overall system performance (end-to-end) that users actually experience also improved by up to 1.54x. Source 3 In particular, it is receiving great attention from the tech world, showing a speed 1.41x faster than the existing standard method, ‘bitsandbytes’. Source 1, Source 8
Currently, this technology has been released as open source and is being verified by many developers, and it shows consistent speed improvement effects regardless of what hardware is used or the size of the model. Source 12
What’s next?
This achievement confirms once again how important software optimization that efficiently handles hardware resources is for the democratization of AI. It seems that more AI libraries and services will adopt such Triton-based optimized kernels in the future. If the AI services we use gradually operate lighter and faster, the era where we can run much smarter AI directly on personal devices without expensive equipment will arrive even sooner.
MindTickleBytes’ AI Reporter Perspective
This case didn’t just slightly increase speed; it proved that how efficiently you handle hardware resources can change the future of AI. In an era where AI models are getting bigger and heavier, it is impressive to see the limits being exceeded with ‘more precise software’ instead of looking for ‘better hardware.’ Performance is ultimately completed by the precision of the software.
References
- Show HN: Fast NF4 dequantization Triton kernel (1.41x faster than bitsandbytes) - https://github.com/Griffith-7/nf4-triton-kernel
- Accelerating NF4 Double-Dequantization within a Single Triton Kernel - https://medium.com/@samdj0245/accelerating-nf4-double-dequantization-within-a-single-triton-kernel-f26a0f35b372
- Fast NF4 Dequantization Kernels for Large Language Model Inference (arXiv:2604.02556) - https://arxiv.org/abs/2604.02556
- NF4 Dequantization Speedup in Triton with Fused Kernel (LinkedIn) - https://www.linkedin.com/posts/jash-dalvi_hi-everyone-i-wrote-a-deep-dive-on-nf4-dequantization-activity-7441491596753203200-Sxzh
- Fast NF4 Dequantization Kernels for Large Language Model Inference (PDF) - https://arxiv.org/pdf/2604.02556
- Paper page - Fast NF4 Dequantization Kernels for Large Language Model Inference (HuggingFace) - https://huggingface.co/papers/2604.02556
- GitHub - Griffith-7/bitnet - https://github.com/Griffith-7/bitnet/blob/main/
- ShowHN: Fast NF4 dequantization Triton kernel (1.41x faster than bitsandbytes) (ModernOrange) - https://modernorange.io/item/48920706
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VueHN2.0 ShowHN: Fast NF4 dequantization Triton kernel - https://vue-hackernews-ssr-5cavbdjcta-ew.a.run.app/item/48920706 - Accelerating Triton Dequantization Kernels for GPTQ – PyTorch - https://pytorch.org/blog/accelerating-triton/
- Fast NF4 Dequantization Kernels for Large Language Model Inference (HTML) - https://arxiv.org/html/2604.02556
- Fast NF4 Dequantization Kernels for Large Language Model Inference (HyperAI) - https://hyper.ai/en/papers/2604.02556
- Fast NF4 Dequantization Kernels for Large Language Model Inference (HTML v1) - https://arxiv.org/html/2604.02556v1
- From Zero to 1.55x: Writing My First Triton Kernel for NF4 - https://numb3r33.github.io/experiments/deeplearning/unsloth/triton/gpu/2025/12/21/unsloth-triton-kernels.html
- Increasing the number of GPUs
- Loading the reference table into shared memory and accessing it directly
- Compressing AI models even smaller
- Up to 1.41x kernel speed improvement
- 99% accuracy improvement
- 50% power consumption reduction
- A language for training AI models
- A tool that helps make GPU optimization easier
- A program that encrypts data