https://vqv.me/t/llm-inference/ Recent public signals for LLM Inference, refreshed every 4 hours. Thu, 18 Jun 2026 17:20:28 +0000 https://vqv.me/t/llm-inference/#signal-776917c674 https://vqv.me/t/llm-inference/#signal-776917c674 Wed, 17 Jun 2026 09:31:45 +0000 The growth of LLM inference workloads is increasing data-center energy demands, challenging existing energy management under stricter grid and demand response conditions. New approaches using quantization offer enhanced demand response capabilities beyond traditional workload shifting and energy as... Why this is here: RISING + 95 signal strength + high ranking score + source-backed + recent this week. Source: arXiv. Original: http://arxiv.org/abs/2606.18851v1 RISING https://vqv.me/t/llm-inference/#signal-383734fa3e https://vqv.me/t/llm-inference/#signal-383734fa3e Tue, 16 Jun 2026 19:25:37 +0000 LLM inference faces challenges due to extreme length variability, making size-based scheduling unreliable. Tail-aware scheduling addresses issues with prediction-driven policies that struggle under distribution shifts, bursty arrivals, and GPU memory pressure. Why this is here: RISING + 95 signal strength + high ranking score + source-backed + recent this week. Source: arXiv. Original: http://arxiv.org/abs/2606.18431v1 RISING https://vqv.me/t/llm-inference/#signal-24a8d49df4 https://vqv.me/t/llm-inference/#signal-24a8d49df4 Wed, 17 Jun 2026 05:51:14 +0000 Distributed multimodal large language model (MLLM) inference frameworks reduce hardware demands by connecting consumer devices, but intermediate embeddings can leak private image prompts. This extends privacy risks beyond text to rich visual and semantic content in image inputs. Why this is here: RISING + 95 signal strength + source-backed + recent this week + low-noise result. Source: arXiv. Original: http://arxiv.org/abs/2606.18710v1 RISING https://vqv.me/t/llm-inference/#signal-37574566b0 https://vqv.me/t/llm-inference/#signal-37574566b0 Mon, 15 Jun 2026 07:35:20 +0000 Modern CPUs increasingly integrate matrix extensions, such as Arm Scalable Matrix Extension (SME), that provide high-throughput matrix execution within the CPU. For LLM inference, however, these units are not a universal replacement for conventional CPU cores... Why this is here: 95 signal strength + source-backed + recent this week + low-noise result. Source: arXiv. Original: http://arxiv.org/abs/2606.16332v1 WATCH https://vqv.me/t/llm-inference/#signal-14663b7df5 https://vqv.me/t/llm-inference/#signal-14663b7df5 Mon, 15 Jun 2026 02:34:15 +0000 Hacker News discussion with 2 points and 0 comments. Why this is here: high signal strength + recent this week + low-noise result. Source: Hacker News. Original: https://www.glukhov.org/observability/monitoring-llm-inference-prometheus-grafana/ WATCH https://vqv.me/t/llm-inference/#signal-0aff5a91c6 https://vqv.me/t/llm-inference/#signal-0aff5a91c6 Wed, 17 Jun 2026 06:36:40 +0000 Current large language model (LLM) inference systems universally deploy ultra-large-scale models using a combination of Tensor Parallelism (TP) and Pipeline Parallelism (PP). However, existing systems treat the model parallelism topology as a static configura... Why this is here: 91 signal strength + source-backed + recent this week + low-noise result. Source: arXiv. Original: http://arxiv.org/abs/2606.18741v1 WATCH https://vqv.me/t/llm-inference/#signal-99a6338cbb https://vqv.me/t/llm-inference/#signal-99a6338cbb Wed, 17 Jun 2026 06:55:49 +0000 Hacker News discussion with 1 points and 0 comments. Why this is here: recent this week + low-noise result. Source: Hacker News. Original: https://github.com/tictacguy/embershard WATCH