Search: “hardware”

arXiv:2610.15514v1 Announce Type: new Abstract: Structured pruning reduces the inference cost of large language models (LLMs) by permanently removing neurons and weight rows, yielding dense, hardware-

Square is launching new hardware: a big kiosk for ordering things that has a touchscreen and lets you talk to an AI. It's called the Square Kiosk. Square says the Kiosk, which has a large pill shape,

arXiv:2610.13708v1 Announce Type: new Abstract: Long-context serving runs out of KV memory before the hardware reaches its useful throughput. HiSparse stores history in host memory and fetches only th

arXiv:2507.19608v2 Announce Type: replace Abstract: On-device LLM inference is increasingly important for edge platforms but remains challenging due to high computational and memory demands and tight

I'm trying to figure out the best long-term upgrade path for my setup. Right now my GPUs are spread across several consumer PCs: Main: 5900X, 32GB DDR4, 2× RTX 3090 24GB. Secondary: i3-10320, 16GB DDR

The semiconductor industry is entering a new era of hardware engineering as artificial intelligence accelerates demand for more powerful chips, interconnected systems and faster product development. B

For what it's worth. I'm a mechanical engineer by education and software engineer in practice over the past ~20 years. Playing around with local models has been a recent hobby. Today I decided to do a

Spent the past month tweaking and experimenting with many different numbers to achieve 30tps. Hardware: -Rx6700xt 12gb vram (AMD) -2x16 ddr4 3200 ram -r5 5600x -llama.cpp vulkan sdk -window11 (no wsl

arXiv:2610.11748v1 Announce Type: cross Abstract: U-Net inference for brain-tumor segmentation requires billions of multiply-accumulate operations, motivating hardware that can reduce computation dyna

Coding-agent platforms, sandbox products, and code-execution services all need the same thing: isolated machines that start up fast, retain their state between bursts of work, and don’t consume comput

I’m not going to call the bursting of artificial intelligence bubble yet, not with all that money still pouring into every hardware and software company with AI in their pitch decks. But we got a litt

Hello all! Thank you to everyone that contributed their feedback and notes for LlamAmpere the last time I posted. I've continued to chip away at improvements for the 3090 crowd -- this release is mode

SlopSoup TV is a live, never-ending pixel-art TV network. Every script, character, voice, camera cut and schedule decision is made by models. It's made to be very absurd, dark and strange. Hardware: o

TL;DR: For my use case, mradermacher/Signal-3.8-27B-Terse-Coder.i1-Q4_K_M proved out. Ymmv based on your domain-specific tests. Time taken to solve problems compared to frontier models is massive; esp

arXiv:2610.10351v1 Announce Type: cross Abstract: Differentiable quantum architecture search (DQAS) is a promising framework for the automated design of quantum circuits, particularly for variational

arXiv:2602.22647v3 Announce Type: replace-cross Abstract: Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation. However, industrial recommender systems often benefit fr

arXiv:2610.07561v1 Announce Type: new Abstract: Image-guided surgery (IGS) depends on accurate tracking of surgical instruments to provide real-time navigation relative to anatomical structures. Comme

arXiv:2610.08527v1 Announce Type: new Abstract: Hybrid architectures combining linear sequence models with softmax attention provide an effective balance between efficient long-context modeling and pr

arXiv:2610.08346v1 Announce Type: new Abstract: Polarization imaging provides physical cues beyond intensity imaging but typically requires specialized hardware. Recent methods infer polarization from

arXiv:2610.08403v1 Announce Type: new Abstract: Large Language Models (LLMs) require substantial computational resources, limiting their deployment on resource-constrained hardware. Ternary LLMs mitig

arXiv:2610.07396v1 Announce Type: new Abstract: Navigation for humanoid robots is critical, yet large-scale evaluation on physical hardware is often impractical due to cost and safety concerns, making

AI is driving the biggest infrastructure build-out in history, with @Google alone forecasting $200B of capex in 2026E. What are the real constraints on a system at this scale? Amin Vahdat is the chief

arXiv:2511.19935v3 Announce Type: replace Abstract: Deploying domain-specialized large language models on resource-constrained hardware motivates reducing both adaptation cost and the number of retain

arXiv:2610.06306v1 Announce Type: new Abstract: General purpose language models are increasingly able to control robotic hardware. Understanding the capabilities and safety of these models when embodi

arXiv:2610.04543v1 Announce Type: cross Abstract: Quantum key distribution proves its protocol secure and says nothing about the hardware beneath it, so military and government operators fielding it f

arXiv:2603.02348v2 Announce Type: replace Abstract: An agent meets a model as served: through an endpoint with a price card, a shared cache and other tenants, or on whatever hardware a self-hosted mod

arXiv:2610.03402v1 Announce Type: new Abstract: To deploy deep neural networks on edge hardware, highly efficient inference schemes are necessary that retain high accuracy. This work presents W16A16,

arXiv:2610.02497v1 Announce Type: new Abstract: Electromyography (EMG) signals vary substantially across individuals, body regions, recording sessions, and sensing hardware, limiting the generalizatio

arXiv:2610.03218v1 Announce Type: new Abstract: Microscaling (MX) formats are emerging as a hardware-supported approach to efficient training and inference. They combine low-precision elements with sh

Automated lint: 19 errors, 64915 warnings, 3 info

Workflow & Hardware The workflow was originally based on Pixaroma’s ComfyUI workflow. I made some modifications and added Sol Attn optimization and Comfy Kitchen Sink, including the very useful Model

As open-weight models close in on the performance of proprietary frontier systems, enterprises are gaining a credible way to run generative AI on hardware they own. That shift puts the data already si

arXiv:2506.11030v2 Announce Type: replace-cross Abstract: Training neural networks has traditionally relied on backpropagation (BP), a gradient-based algorithm that, despite its widespread success, su

arXiv:2610.02089v1 Announce Type: cross Abstract: As robotic hardware and learning methods advance, humanoids need tools to perform tasks beyond their inherent physical limits. Successful tool use req

arXiv:2610.00586v1 Announce Type: cross Abstract: Activation memory, not compute, limits CNN inference on constrained hardware such as microcontrollers. Direct in-place convolution removes the dual-bu

arXiv:2609.38980v1 Announce Type: new Abstract: Radar hardware faults threaten automated perception, motivating accurate, compact diagnosis and understandable maintenance guidance. We introduce SCORE-

arXiv:2609.36931v1 Announce Type: new Abstract: Reproducibility is essential for scientific research, yet prior work shows that LLM outputs vary with hardware and batching. We identify an overlooked f

Follow-up to my post from about two weeks ago. A lot has changed since then, so I wanted to post an update on where the backend is now. Where the green architectures stand When I posted last time, 7 a

arXiv:2609.37855v1 Announce Type: cross Abstract: Hand anthropometry supports protective-glove design, but existing measurement methods often require trained operators, specialized hardware, or manual

arXiv:2609.38006v1 Announce Type: new Abstract: Running a language model locally offers advantages in privacy, latency, and cost, but local hardware fits only small models, which are less capable than

arXiv:2603.15106v2 Announce Type: cross Abstract: Enabling efficient deep neural network (DNN) inference on edge devices with different hardware constraints is a challenging task that typically requir

I’m building a Polish General purpose legal Model that drafts documents, answers questions using legal sources, and has enough coding ability to handle some automation. The workflow is very tool-heavy

arXiv:2609.34586v1 Announce Type: cross Abstract: Deploying LLM-enabled agentic applications across the Edge-to-Cloud continuum remains challenging due to hardware heterogeneity, deployment complexity

arXiv:2609.34142v1 Announce Type: new Abstract: Continuous video analytics requires accurate localization at low latency within embedded power budgets. This paper presents a hardware-software design m

arXiv:2609.31659v1 Announce Type: new Abstract: Power magnetic materials are characterized on the sinusoidal and triangular waveforms that excitation hardware conveniently produces, whereas deployed c

Hugging Face Jobs usage is stonking 📈 All thanks to agents: you just need the hf CLI installed and you have access to an insane amount of hardware. Asked mine to test hundreds of code snippets from Hu