As the state-of-the-art GLM 4.7 model continues to lead in coding performance, Novita AI remains committed to delivering a reliable, efficient, and production-grade GLM service to our users.
Learn how speculative sampling and low-precision quantization reduce costs and accelerate speed, offering practical solutions for scalable AI deployment.
Novita AI speeds up Llama-70B loading with KV sparsity, reducing memory, computation, and I/O overhead for faster inference and minimal accuracy loss.
Discover how to select cost-effective GPUs for large model inference, focusing on performance metrics and best practices to enhance efficiency.
Boost AI inference speed with KV sparsity. Understand how it works and optimize your models for real-world applications.
Currently, to schedule GPU Pods in Kubernetes (k8s), various extension solutions are put into action, including Device Plugin, Extended Resource,
Port mapping is a crucial aspect of developing and deploying containerized applications. Typically, we establish a connection between a container’s
Introduction to Optimizing CPU and GPU Performance In high-performance computing and large-scale parallel task processing, GPUs have become indispensable accelerators.
Mitchell Stern et al. 2018 introduced the prototype concept of speculative decoding. This method has since been further developed and
Discover how selecting the best data types and optimizing GPU hardware support unlocks new pathways for spending up quantization inference.
