FlashQLA: 3× Faster Linear Attention on NVIDIA Hopper GPUs (2026)
FlashQLA, a new linear attention kernel library from the QwenLM team, achieves up to 3× speedup on NVIDIA Hopper GPUs by optimizing Gated Delta Network chunked prefill operations. The open-source library, built on TileLang, targets both large-scale AI training and edge inference.
FlashQLA: 3× Faster Linear Attention on NVIDIA Hopper GPUs (2026)
summarize3-Point Summary
- 1FlashQLA, a new linear attention kernel library from the QwenLM team, achieves up to 3× speedup on NVIDIA Hopper GPUs by optimizing Gated Delta Network chunked prefill operations. The open-source library, built on TileLang, targets both large-scale AI training and edge inference.
- 2FlashQLA: 3× Faster Linear Attention on NVIDIA Hopper GPUs (2026) FlashQLA, a high-performance linear attention kernel library developed by the QwenLM team, delivers up to 3× speedup in forward and backward passes for Gated Delta Network (GDN) chunked prefill operations on NVIDIA Hopper GPUs.
- 3This breakthrough tackles computational bottlenecks in transformer models, enabling faster pretraining and efficient edge-side AI inference — all while reducing power consumption and latency.
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FlashQLA: 3× Faster Linear Attention on NVIDIA Hopper GPUs (2026)
FlashQLA, a high-performance linear attention kernel library developed by the QwenLM team, delivers up to 3× speedup in forward and backward passes for Gated Delta Network (GDN) chunked prefill operations on NVIDIA Hopper GPUs. This breakthrough tackles computational bottlenecks in transformer models, enabling faster pretraining and efficient edge-side AI inference — all while reducing power consumption and latency.
How TileLang Enables Hopper GPU Optimization
FlashQLA is built on TileLang, a domain-specific language designed for fine-grained GPU kernel development. TileLang allows precise control over CUDA thread scheduling, memory coalescing, and shared memory usage, eliminating kernel launch overhead and maximizing Hopper’s third-generation Tensor Core utilization. This results in near-peak hardware efficiency even under complex attention patterns.
Gated Delta Network Performance Benchmarks
Unlike traditional softmax attention, FlashQLA’s Gated Delta Network (GDN) replaces quadratic scaling with linear approximations, making it ideal for sequences beyond 32K tokens. Internal benchmarks show consistent 2.5–3× latency reduction across batch sizes 1–128, with peak gains in long-context dialogue tasks. Qwen3 models integrated with FlashQLA report 2.8× faster prefill times in multi-turn conversations.
Why FlashQLA Outperforms Flash Attention 2–4
While Flash Attention pioneered tiled computation and memory-reduction strategies, FlashQLA is the first library optimized specifically for gated architectures. It integrates delta updates and gating mechanisms to stabilize gradient flow — a critical gap left unaddressed by Flash Attention 2–4 and other open-source kernels. Reverse-engineering efforts by Modal.com confirm this shift toward hardware-aware, architecture-specific attention kernels.
Seamless Integration for Enterprise and Edge AI
FlashQLA offers native compatibility with Hugging Face Transformers and vLLM, with full documentation and benchmarking scripts available on GitHub. No proprietary dependencies — just pure, open-source acceleration. Deployable on edge devices, autonomous systems, and real-time translation agents, it reduces inference costs by up to 40% compared to softmax-based alternatives.
As context lengths surpass 100K tokens, FlashQLA’s linear attention kernel library provides the only scalable, open path to efficient transformer inference. With no licensing restrictions and full Hopper GPU support, it’s becoming the new standard for next-gen AI systems.
