TorchAO: A PyTorch-Native Shortcut To Smaller, Faster Models TorchAO is PyTorch's native toolkit for model efficiency: it unifies post-training quantization (PTQ), quantization-aware training (QAT), float8 (FP8) training, and structured sparsity in one coherent... deep learning FP8 model efficiency open source PyTorch QAT quantization sparsity TorchAO
NVFP4 Is Transforming AI Training: 4-Bit Precision Meets High Performance Efficiently training massive language models is now a central challenge for organizations building advanced AI systems. As models grow larger and datasets expand into the trillions of tokens, the need... AI training Blackwell architecture generative AI large language models low precision model efficiency NVFP4 quantization
IBM Granite 4.0 Enterprise AI: Performance, Efficiency, and Trust IBM’s Granite 4.0 models are setting a new benchmark for enterprise AI by blending exceptional efficiency with top-tier performance. The innovative hybrid Mamba/transformer architecture dramatically r... AI benchmarks AI security enterprise AI hybrid AI IBM Granite language models Mamba architecture model efficiency
Smarter LLMs: How the vLLM Semantic Router Delivers Fast, Efficient Inference Large language models are evolving rapidly. Instead of simply increasing their size, innovators now focus on maximizing efficiency, reducing latency, and assigning compute resources according to query... enterprise AI Kubernetes latency optimization LLM inference model efficiency open source AI semantic routing
Qwen3-Next and vLLM: Advancing Efficient Long-Context AI with Hybrid Architecture AI is evolving rapidly, and efficiency is key for effective large-scale deployment. Qwen3-Next, the latest model from the Qwen team, pushes the boundaries with a hybrid architecture purpose-built for ... GPU optimization hybrid attention long-context AI model efficiency MoE multi-token prediction Qwen3-Next vLLM integration