Knowledge catalogue
arXiv:2605.06763v1 Announce Type: new Abstract: Sparse attention improves LLM inference efficiency by selecting a subset of key-value entries, but at the cost of potential accuracy degradation. In par
arXiv:2509.05276v4 Announce Type: replace-cross Abstract: Mainstream Transformer-based large language models face major efficiency bottlenecks: training computation scales quadratically with sequence
arXiv:2605.06927v1 Announce Type: cross Abstract: Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception f
Great collab with @SakanaAILabs on an #ICML26 paper about sparse transformer kernels + formats optimized for modern NVIDIA GPU execution. • TwELL sparse packing • Fused CUDA kernels • 20%+ inference/t
We’ve rolled out a significant update to Google Kubernetes Engine (GKE) that solves one of the most annoying problems in cloud infrastructure: cold start latency. GKE now has up to 4x faster node star
This article discusses optimization techniques for maximizing efficiency on NVIDIA's GB200 NVL72 system using Slurm block scheduling, a job scheduling approach designed to improve resource utilization
arXiv:2605.04357v1 Announce Type: cross Abstract: The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide
arXiv:2605.05049v1 Announce Type: cross Abstract: Frontier models increasingly adopt Mixture-of-Experts (MoE) architectures to achieve large-model performance at reduced cost. However, training MoE mo
Nvidia Corp. will help publicly traded glass maker Corning Inc. boost the rate at which it produces parts for optical data center networks. The partnership, which the companies announced today, will s
arXiv:2605.01352v1 Announce Type: cross Abstract: GPU-based simulation environments for embodied AI interleave physics simulation (CUDA) and photorealistic rendering (Vulkan) on a single device. We ob
arXiv:2603.18066v2 Announce Type: replace-cross Abstract: Backpropagation has enabled modern deep learning but is difficult to realize as an online, fully distributed hardware learning system due to g
arXiv:2602.07264v2 Announce Type: replace Abstract: Unmanned aerial vehicles are rapidly transforming multiple applications, from agricultural and infrastructure monitoring to logistics and defense. I
arXiv:2602.02958v4 Announce Type: replace Abstract: Despite rapid progress in autoregressive video diffusion, an emerging system algorithm bottleneck limits both deployability and generation capabilit
Panthalassa, a US startup, is developing floating data centers powered by ocean waves and cooled by seawater to address AI infrastructure's growing energy demands. Peter Thiel led a $140 million inves
arXiv:2605.00174v1 Announce Type: cross Abstract: Video and image streaming on edge devices requires low latency. To address this, Neural Networks (NNs) are widely used, and prior work mainly focuses
This article examines how value in the AI industry is shifting away from traditional chip manufacturers toward model development labs and companies that build large language models. It likely analyzes
All the Doomers and hawks are lining up behind this distillation 'attack' farce because they want to see open source banned. It's really as simple as that. They want to take away your right to choose,
arXiv:2604.27085v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe intercon
arXiv:2604.26968v1 Announce Type: cross Abstract: Key-value (KV) cache memory management is the primary bottleneck limiting throughput and cost-efficiency in large-scale GPU inference serving. Current
arXiv:2604.25326v2 Announce Type: replace-cross Abstract: Speculative decoding enhances the inference efficiency of large language models (LLMs) by generating drafts using a small draft language model
arXiv:2604.26103v1 Announce Type: cross Abstract: All current LLM serving systems place the GPU at the center, from production-level attention-FFN disaggregation to NVIDIA's Rubin GPU-LPU heterogeneou
The TensorRT for RTX plugin provides a runtime for Unreal Engine's Neural Network Engine (NNE), enabling efficient deployment of AI models directly within real-time applications. Developers can see 1.
arXiv:2603.15954v2 Announce Type: replace Abstract: Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The
arXiv:2604.24169v1 Announce Type: new Abstract: 3D point cloud perception remains tightly coupled to custom CUDA operators for spatial operations, limiting portability and efficiency on non-NVIDIA, AM
arXiv:2505.02922v3 Announce Type: replace Abstract: Recent large language models (LLMs) are rapidly extending their context windows, yet inference throughput lags due to increasing GPU memory and band
arXiv:2604.21743v1 Announce Type: new Abstract: Image enhancement models for mobile devices often struggle to balance high output quality with the fast processing speeds required by mobile hardware. W
arXiv:2604.16395v2 Announce Type: replace-cross Abstract: Context retrieval systems for LLM inference face a critical challenge: high retrieval latency creates a fundamental tension between waiting fo
arXiv:2412.03594v3 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly play an important role in a wide range of information processing and management tasks in industry. M
Last night was the biggest disaster in the history of Tesla. Let me walk you through what actually happened on that earnings call, because the headlines are doing you a disservice: Elon Musk got on th
arXiv:2604.20819v1 Announce Type: new Abstract: The scalability of long-context large language models is fundamentally limited by the quadratic memory cost of exact self-attention, which often leads t
arXiv:2604.18616v1 Announce Type: cross Abstract: LLM-based coding agents can generate functionally correct GPU kernels, yet their performance remains far below hand-optimized libraries on critical co
NVIDIA AI and accelerated computing are advancing sustainability, climate science and energy efficiency through five key applications. These include RecycleOS, an AI and robotics solution that helps r
arXiv:2412.00069v3 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) has garnered significant attention for its ability to scale up neural networks while utilizing the same or even fewer
arXiv:2604.17808v1 Announce Type: cross Abstract: Zero-knowledge proof (ZKP) provers remain costly because multi-scalar multiplication (MSM) and number-theoretic transforms (NTTs) dominate runtime as
arXiv:2602.11092v2 Announce Type: replace Abstract: Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmi
arXiv:2510.04008v5 Announce Type: replace Abstract: Softmax Attention has a quadratic time complexity in sequence length, which becomes prohibitive to run at long contexts, even with highly optimized
arXiv:2604.16715v1 Announce Type: cross Abstract: Graph foundation models have demonstrated remarkable adaptability across diverse downstream tasks through large-scale pretraining on graphs. However,
arXiv:2604.18067v1 Announce Type: new Abstract: The miniaturisation of neural processing units (NPUs) and other low-power accelerators has enabled their integration into microcontroller-scale wearable
arXiv:2604.17050v1 Announce Type: new Abstract: With the rapid development of embodied intelligence, robotics education faces a dual challenge: high computational barriers and cumbersome environment c
arXiv:2604.15769v1 Announce Type: cross Abstract: Spiking transformers achieve competitive accuracy with conventional transformers while offering 38-57imes energy efficiency on neuromorphic hardware,
arXiv:2603.03188v2 Announce Type: replace-cross Abstract: We introduce a novel framework for uncertainty quantification in clustering that combines martingale posterior distributions with density-base
We’re launching Kimi K2.6 on Fireworks as a Day-0 launch partner! K2.5 was the base for standout models like @Cursor’s Composer 2 and was the most popular model on our training platform. K2.6 on Firew
arXiv:2604.13048v1 Announce Type: cross Abstract: Modern cloud-native platforms expose thousands of time series metrics through systems like Prometheus, yet formulating correct queries in domain-speci
arXiv:2511.19204v3 Announce Type: replace Abstract: We present a sampling-based model predictive control (MPC) framework that enables emergent locomotion without relying on handcrafted gait patterns o
Heterogeneous computing, which combines multiple types of GPUs and processors, is becoming standard in enterprise environments to handle diverse workloads more efficiently than traditional homogeneous
This Reddit post from r/ollama discusses a user's experience running Cline (an AI coding agent) with Ollama on a high-end local hardware setup consisting of an NVIDIA RTX 4090 with 24GB VRAM and an In
This Reddit thread from r/StableDiffusion discusses a performance issue where Roop Unleashed version 4.3.1 — a face-swapping tool commonly used alongside Stable Diffusion — fails to fully utilize the
A Reddit post from r/ollama in which a user shares their experience running a 31B parameter model locally using Ollama, reflecting on the surprisingly demanding hardware and infrastructure requirement
🆕 The Full Story of Notion AI https://latent.space/p/notion We're so excited to chat with @simonlast and @sarahmsachs about Notion's 'Token Town' - the crack team of AI Engineers and Model Behavior En
arXiv:2604.11615v1 Announce Type: cross Abstract: Matrix extensions have emerged as an essential feature in modern CPUs to address the surging demands of AI workloads. However, existing designs often
arXiv:2604.10170v1 Announce Type: cross Abstract: The growing complexity of visuomotor policies poses significant challenges for deployment with heterogeneous robotic hardware constraints. However, mo
arXiv:2604.10539v1 Announce Type: cross Abstract: Key-Value (KV) cache plays a crucial role in accelerating inference in large language models (LLMs) by storing intermediate attention states and avoid
NVIDIA Ising is the world's first family of open-source quantum AI models, designed to help researchers and enterprises build quantum processors capable of running useful applications. The family span
arXiv:2604.11625v1 Announce Type: cross Abstract: Neural operators have emerged as powerful surrogates for partial differential equation (PDE) solvers, yet they are typically trained as monolithic mod
arXiv:2604.10180v1 Announce Type: cross Abstract: Disaggregation maps parts of an AI workload to different types of GPUs, offering a path to utilize modern heterogeneous GPU clusters. However, existin
arXiv:2604.09943v1 Announce Type: new Abstract: Reservoir computing (RC) is a computational framework known for its training efficiency, making it ideal for physical hardware implementations. However,
This Reddit thread from r/ollama discusses community recommendations for purchasing or renting GPU servers to run Ollama and local LLMs. It likely covers options ranging from dedicated GPU server prov
Soumith Chintala shared a post highlighting that $200 per month is sufficient to rent access to an NVIDIA H100 GPU for approximately 6 hours every workday, making high-end AI compute more accessible t
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"**AI Engineer Europe (London) — Conference Recap**