Neural Magic Unveils Machete: A New Mixed-Input GEMM Kernel for NVIDIA Hopper GPUs

**Challenges with Large Language Models (LLMs)** Large language models like GPT-3 and Llama face significant challenges with memory use and speed. As these models become larger, they require more computing power, making it essential to use hardware efficiently. **Memory and Speed Issues** These models often need a lot of memory and can be slow in providing responses. This is particularly evident with NVIDIA Hopper GPUs, where achieving a balance between memory and speed can be challenging. **Introducing Machete by Neural Magic** Neural Magic has developed Machete, an innovative solution for NVIDIA Hopper GPUs. Machete greatly reduces memory usage while ensuring high performance. **Key Benefits of Machete** - **Memory Efficiency:** Cuts memory needs by about 4 times, which is vital for larger models. - **Speed Improvement:** Delivers performance similar to FP16 precision but uses memory more effectively. - **Faster Inference:** Increases the speed of model inference, overcoming computing limitations. **Technical Innovations** Machete uses advanced technology, including special tensor core instructions and weight pre-shuffling to enhance performance. **How Machete Works** - **Weight Pre-Shuffling:** Lowers memory load times, improving overall speed and reducing delays. - **Upconversion Routines:** Efficiently converts 4-bit data to 16-bit, optimizing resource use. **Machete’s Value in Real-World Applications** Machete allows large LLMs to run efficiently on existing hardware. In tests, it showed a 29% increase in input speed and a 32% faster output generation for Llama 3.1 70B, achieving excellent performance. **Performance Highlights** - **Input Speed:** 29% faster for Llama 3.1 70B. - **Output Speed:** 32% quicker, with response times under 250ms on a single H100 GPU. - **Scalability:** 42% speed improvement when using a 4xH100 setup for Llama 3.1 405B. **Conclusion** Machete is a significant advancement for optimizing LLM inference on NVIDIA Hopper GPUs. It addresses memory and bandwidth challenges, streamlining the demands of large models while reducing computing costs. Machete is poised to change how LLMs are used, providing faster and more efficient outputs without sacrificing quality. **Get Connected!** For more insights and updates, follow us on Twitter and join our Telegram Channel and LinkedIn Group. Subscribe to our newsletter and engage with our growing ML community. **Explore AI Solutions** Stay competitive by discovering AI opportunities for your business. Connect with us for guidance on implementing effective AI strategies.