Knowledge catalogue
Yoyo I renamed https://github.com/syv-ai/qwen38-27b-rtx3090 to https://github.com/syv-ai/HyperQwen/ to focus more on Qwen models and 'local' hardware in general, than just a single card and a single m
arXiv:2609.08729v1 Announce Type: new Abstract: The transition from single-core to multi-core architectures in safety-critical embedded systems introduces significant challenges due to inter-core inte
arXiv:2609.09971v1 Announce Type: new Abstract: Finger-specific motor intent is a clinically meaningful control signal for post-stroke neurorehabilitation, where residual muscle activity may remain me
arXiv:2609.05161v1 Announce Type: cross Abstract: Rigid Body Dynamics (RBD) forms the computational core of real-time robotic control, but its immense computational complexity creates a performance bo
Hi, I am thinking about putting some money / time into my local ai learning path and 'cleared' my attic where I found the following hardware. 3 x NUC11 (Core i5 1145G7 / 2,6 GHz, 64 GB RAM DDR4 2666MH
arXiv:2609.02684v1 Announce Type: new Abstract: Deploying 3D point cloud models on edge hardware such as the NVIDIA Jetson Orin Nano is severely constrained by compute and memory budgets. Existing com
arXiv:2608.30439v1 Announce Type: cross Abstract: Inference with transformer-based large language models (LLMs) is often limited by the memory-bound KV cache and quadratic attention cost. State-space
arXiv:2608.27199v1 Announce Type: new Abstract: Vision generative artificial intelligence (AI) has emerged as one of the most rapidly advancing areas of deep learning. The explosion of multimodal mode
arXiv:2608.23807v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) can in principle generate text faster than autoregressive (AR) models, since they denoise many tokens at once.
Qwen 3.8 27B now runs locally inside RunAnywhere! Your app can use the same local AI setup across different hardware. On Mac it runs Swift + mlx. On Windows, Electron + llama.cpp. Different architectu
arXiv:2601.17178v3 Announce Type: replace-cross Abstract: Hardware Trojans (HTs) remain a critical threat because learning-based detectors often overfit to narrow trigger/payload patterns and small, s
arXiv:2608.14306v1 Announce Type: new Abstract: This paper presents a coordination architecture for heterogeneous UAV/UGV swarms that synthesises mission actions from uncertain, multi-modal sensor evi
arXiv:2608.11271v1 Announce Type: cross Abstract: Next-generation Synthetic Aperture Radar (SAR) missions will generate data far faster than they can downlink, making onboard data reduction essential
arXiv:2608.11802v1 Announce Type: cross Abstract: Methods to increase the resilience of systems to cyber-attacks become increasingly important. Control-flow monitoring provides a principled basis to e
arXiv:2608.07582v1 Announce Type: new Abstract: Unplanned network hardware malfunctions can interrupt services and result in expensive downtime in data centers. A deep learning-based predictive mainte
arXiv:2608.06130v1 Announce Type: cross Abstract: AI agents performing cryptographic operations (signing Git commits, authenticating API calls, issuing certificates) currently store private keys in so
Anthropic is hiring a custom silicon team to design proprietary chips that will power its Claude models, while still planning a multi‑chip strategy that mixes internally designed hardware with compone
arXiv:2608.02712v1 Announce Type: cross Abstract: Kernel generation for hardware accelerators such as GPUs and NPUs has become a proving ground for large language models (LLMs), and state-of-the-art s
I have been working on this tool for months and there are a lot of new functionalities and tests that are going to be released in the next few weeks! The goal of the tool is to allow community members
arXiv:2607.20943v1 Announce Type: cross Abstract: Quantum Phase Estimation (QPE) is a foundational algorithm for molecular ground-state energy estimation, but its deep circuit requirements make direct
Open weights = freedom. You can run them on your own hardware. No vendor can pull the plug. No API can deprecate you. No company logs your private data. That's sovereignty. Closed models hand one comp
Apple has sued OpenAI, alleging that engineers stole Apple secrets to advance the AI startup's hardware plans. In its complaint, Apple says it uncovered 'a pattern of theft of Apple's trade secrets by
arXiv:2607.05281v1 Announce Type: cross Abstract: Present-day quantum computing is cloud-based, where a user submits a circuit to a service provider's proprietary backend hardware. While providers may
Yann LeCun shared details about an early AI prototype project developed over 9 months by a small team in Paris at UMA_Rob, covering artificial intelligence, software, and hardware components. The proj
arXiv:2607.02376v1 Announce Type: new Abstract: Recent advances in agentic AI are producing increasingly complex autonomous systems that integrate large language models, world models, optimization eng
arXiv:2407.15283v2 Announce Type: replace-cross Abstract: Industry is moving toward autonomous, network-connected machines that detect and adapt to changing conditions, including hardware faults. Conv
NVIDIA Confidential Computing addresses data privacy and security concerns for AI workloads by protecting data during inference and engagement with models, offering high-performance protection in loca
arXiv:2606.25277v1 Announce Type: cross Abstract: To address data overload and inefficient shape-level annotation in robotic visual inspection, this paper proposes a hardware-software integrated optoe
arXiv:2509.13793v2 Announce Type: replace-cross Abstract: It is shown that the port behavior of a resistor-diode network corresponds to the solution of a ReLU monotone operator equilibrium network (a
arXiv:2606.21752v1 Announce Type: cross Abstract: Histopathologic cancer detection is challenging due to tissue variability, staining differences, and subtle visual distinctions between disease classe
arXiv:2602.22352v2 Announce Type: replace-cross Abstract: With the continuous growth of neural network scales, low-precision quantization is widely used in edge accelerators. Classic multi-threshold a
Kylie Robison / Core Memory: How startups Westmag, which raised an $11M, a16z-led seed in August 2025, and Atlas are seeking to make actuators, the foundation of hardware, outside of China — Westmag a
This post highlights four recent improvements to the ecosystem of open-weight large language models designed to run efficiently on consumer hardware, covering developments that make local LLM deployme
arXiv:2606.03392v1 Announce Type: new Abstract: Embodied AI in the real world requires both accurate hardware and robust vision-language-action (VLA) policies. We present OpenEAI-Platform, a fully ope
OpenAI Robotics is hiring, looking for exceptional full-stack hardware, ops, systems, and ML engineers to help us program and manufacture robots that are useful for society. AI should be able to help
Hardware firms are cleaning up bigtime as enterprises and cloud providers can’t get enough computing power for their artificial intelligence dreams. Dell Technology’s stock rocketed an incredible 31%
Barratt Dewey / Tectonic Defense: Picogrid, which is building a hardware and software integration layer for military systems, raised a $45M Series A led by Bessemer — Picogrid is proving that teamwork
arXiv:2605.27407v1 Announce Type: cross Abstract: Evaluating fairness in Spiking Neural Networks (SNNs) demands rigorous benchmarks that reflect real-world complexities, yet existing assessments remai
Ben Jiang / South China Morning Post: A look at Xiaomi's AI push to future-proof its hardware and EV ecosystem, as it recently committed ~$8.8B in AI investments over the next three years — Xiaomi is
arXiv:2605.20456v1 Announce Type: cross Abstract: Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capa
I'm excited about the new @amd Ryzen AI Halo because we need more local hardware for AI builders! There's something fun and exciting about building on your own machines rather than sending to the clou
The unit of AI compute has shifted from single hosts to rack-scale systems that integrate NVIDIA GPUs, CPUs, scale-up networking fabrics, and liquid cooling, such as the NVIDIA GB300 NVL72 and NVIDIA
arXiv:2605.19782v1 Announce Type: new Abstract: LLM discovery and optimization systems are increasingly applied across domains, implementing a common propose-evaluate-revise loop. Such optimization or
Bloomberg: Analysts: China's AI hardware suppliers face capacity constraints and component shortages, including optical and electronic chips, that may limit 2026 growth — China's artificial intelligen
AI factory security begins at the hardware layer — a fact that is taking on new urgency as enterprises scramble to secure the infrastructure powering the next computing era. The rise of agentic AI and
arXiv:2604.24397v1 Announce Type: cross Abstract: In the noisy intermediate-scale quantum (NISQ) regime, quantum devices contain hardware-specific noise sources which restrict device-invariant error m
arXiv:2604.23647v1 Announce Type: cross Abstract: In Transformer models, non-GEMM (non-General Matrix Multiplication) operations -- especially Softmax and Layer Normalization (LayerNorm) -- often domi
arXiv:2604.14550v1 Announce Type: cross Abstract: Generating synthesizable Verilog for large, hierarchical hardware designs remains a significant challenge for large language models (LLMs), which stru
A Reddit post on r/StableDiffusion in which a user shares a music video created entirely using AI-generated imagery produced on their own local hardware, likely using tools such as Stable Diffusion wi
To run GLM-5.1 locally (744B params, 40B active MoE), full precision needs ~1.65TB disk + enterprise hardware like 8x H200/B200 GPUs. Minimum practical setup: Unsloth 2-bit GGUF quant (~220-236GB). Fi
It is very cool to see llama.cpp on the big stage in todays Windows event! The software and hardware stacks are finally coming together. Our community has put a lot of hard work in the past years and
arXiv:2610.03127v1 Announce Type: new Abstract: Hardware-aware neural architecture search (NAS) is dominated by evaluation cost: every architecture must be trained before its reward is known. Conforma
After some recommendations for hardware (I'm starting from nothing), in the region of £6-7k. I will be mainly looking to use it for coding and have found Deepseek V4.1 Flash or Qwen3.8 27b good and so
arXiv:2610.03135v1 Announce Type: new Abstract: Serving long-context autoregressive language models is constrained by the key-value (KV) cache. Most dynamic eviction methods score token importance per
Where to even begin on this one...I guess I should start by acknowledging the risk vs reward for vendors other than nvidia, so: Yes I understand that nvidia are dominant currently on speed (llm and im
Day-0 OpenVINO support from @inteldevs! 🥳 Qwen-Image-2.1 is ready to run optimized on Intel hardware. One open-weight checkpoint for both generation and editing. 👇 We're excited to offer Day0 OpenVINO
arXiv:2609.22122v1 Announce Type: new Abstract: Cross-device hardware evaluation often assumes that if architecture rankings transfer across devices, a proxy device can support target-side model selec
So I would like to run qwen 3.8 27b locally for my ai agents, maybe even in parallel with other small LLMs (such as qwen3.5 9b, oss 20b etc..). What is the best hardware to do this? Not a video card b
C/C++ provide direct hardware and memory control with near-zero overhead, delivering the raw speed needed for extreme performance work like AI training stacks. Higher-level languages add layers that c
Zhiye Liu / Tom's Hardware: Nvidia announces the RTX Pro 5500 Blackwell Workstation Edition, offering comparable specs to the RTX 5090 but with 84GB of GDDR7 memory, vs. RTX 5090's 32GB — The GeForce