TileRT AI boosts LLM decode interactivity on NVIDIA Blackwell GPUs

By PulseAugur Editorial · [4 sources] · 2026-08-11 18:07

TileRT, a new technology from TileRT AI, promises to significantly boost decode interactivity for large language models on NVIDIA Blackwell GPUs. By statically compiling models into a persistent Engine Kernel, TileRT aims to overcome latency issues inherent in traditional GPU programming models. This innovation could offer a 1.9x improvement in decode interactivity at the same per-token cost, potentially impacting competitors like Groq, Cerebras, and SambaNova. AI

IMPACT Could improve LLM inference speed and efficiency, potentially impacting the competitive landscape for AI hardware and inference solutions.

RANK_REASON Announcement of a new technology for optimizing LLM performance on specific hardware.

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AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

TileRT AI boosts LLM decode interactivity on NVIDIA Blackwell GPUs

COVERAGE [4]

X — SemiAnalysis TIER_1 English(EN) · SemiAnalysis_ · 2026-08-11 18:07

@TileRT_AI This is where TileRT comes in. Instead of continuously launching kernels or replaying a DAG of kernels, TileRT has the GPU continuously execute a per

@TileRT_AI This is where TileRT comes in. Instead of continuously launching kernels or replaying a DAG of kernels, TileRT has the GPU continuously execute a persistent pipeline, statically compiling the entire model ahead of time into a persistent Engine Kernel: the host launches…
X — SemiAnalysis TIER_1 English(EN) · SemiAnalysis_ · 2026-08-11 18:07

@TileRT_AI The gap comes from latency rather than bandwidth. The traditional GPU programming model launches and synchronizes many individual kernels, whose setu

@TileRT_AI The gap comes from latency rather than bandwidth. The traditional GPU programming model launches and synchronizes many individual kernels, whose setup and teardown overhead becomes significant at ultra-high levels of interactivity. While these latency costs are less vi…
X — SemiAnalysis TIER_1 English(EN) · SemiAnalysis_ · 2026-08-11 18:07

@TileRT_AI An 8-GPU HGX B200 server provides a theoretical HBM memory bandwidth of 64 TB/s. At batch size 1, GLM-5 at NVFP4 requires only ~21 GB of active param

@TileRT_AI An 8-GPU HGX B200 server provides a theoretical HBM memory bandwidth of 64 TB/s. At batch size 1, GLM-5 at NVFP4 requires only ~21 GB of active parameters per generated token. The B200 bandwidth roofline would therefore suggest up to 3,047 tokens/s/user without specula…
X — SemiAnalysis TIER_1 English(EN) · SemiAnalysis_ · 2026-08-11 18:07

ALERT🚨 TILERT from @TileRT_AI TO BOOST DECODE INTERACTIVITY BY 1.9X AT THE SAME PER-TOKEN COST ON THE SAME NVIDIA BLACKWELL GPUs. What does this mean for Groq/

ALERT🚨 TILERT from @TileRT_AI TO BOOST DECODE INTERACTIVITY BY 1.9X AT THE SAME PER-TOKEN COST ON THE SAME NVIDIA BLACKWELL GPUs. What does this mean for Groq/Cerebras/SambaNova? 👇️ 1/5🧵 https://t.co/lTBJA1rgO0

COVERAGE [4]

@TileRT_AI This is where TileRT comes in. Instead of continuously launching kernels or replaying a DAG of kernels, TileRT has the GPU continuously execute a per

@TileRT_AI The gap comes from latency rather than bandwidth. The traditional GPU programming model launches and synchronizes many individual kernels, whose setu

@TileRT_AI An 8-GPU HGX B200 server provides a theoretical HBM memory bandwidth of 64 TB/s. At batch size 1, GLM-5 at NVFP4 requires only ~21 GB of active param

ALERT🚨 TILERT from @TileRT_AI TO BOOST DECODE INTERACTIVITY BY 1.9X AT THE SAME PER-TOKEN COST ON THE SAME NVIDIA BLACKWELL GPUs. What does this mean for Groq/

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