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Nari Qwen3-TTS is a high-performance serving implementation for the Qwen3-TTS model, achieving sub-50ms time-to-first-audio and handling 10 requests per second on a single H100 GPU.
This paper presents CUTLASS-based kernels that fuse SwiGLU activation with GeMM at the tile level, achieving up to 2.47× speedup on NVIDIA H100 for efficient LLM inference.
The article clarifies that GPU performance in AI inference is limited by memory bandwidth rather than compute power, using the NVIDIA H100 as an example to explain GPU architecture and its effect on token generation rates.
This paper presents an analytically structured, empirically calibrated methodology for estimating LLM inference energy on NVIDIA H100 GPUs without direct measurement, separating prefill and decoding phases and decomposing energy into compute, parameter-access, KV-cache write, and attention-read components.
An NYT analysis details the unprecedented global build-out of AI data centers and chips, projecting a tenfold increase in AI computing power by 2028, which is expected to drive major breakthroughs in AI capabilities.
A technical discussion validates TurboQuant performance data on NVIDIA H100 GPUs with FP8 Tensor Cores and promises further insights from non-H100 testing.