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Tapping into the country’s strengths in manufacturing and engineering, NVIDIA is helping advance Korea’s physical AI ecosystem. The country’s industry leaders are applying accelerated computing, AI and simulation to everything from semiconductor manufacturing to robotics and autonomous vehicles. These efforts are helping bring physical AI from development into real-world production. Samsung Advances Semiconductor Engineering With […]
Korea’s Ministry of Science and ICT and NVIDIA hosted the MSIT-NVIDIA AI Startup Partnership Forum at COEX. Ryu Je-myung, vice minister of science and ICT, joined NVIDIA leaders to meet with over 20 Korean startups across foundation models, physical AI, AI infrastructure and AI semiconductors. NVIDIA also announced an investment in Korean startup Point2, with […]
Simulation, which has long been the proving ground for robots learning to walk, roll and hop, is now helping researchers teach robots tasks that require a finer touch: aligning gears, connecting cables and assembling small components. The shift from locomotion to manipulation is taking center stage at this week’s Conference on Robot Learning (CoRL) in […]
We check out the ASRock Rack DMRD16-2L+ which is a 16-channel Intel Diamond Rapids Xeon motherboard with 128x PCIe Gen6 lanes over MCIO The post ASRock Rack DMRD16-2L+ Spotted with 16 DDR5 Slots for Diamond Rapids appeared first on ServeTheHome.
The ggml library has been updated to make tied selections in the radix Top-K algorithm deterministic on CUDA and Metal backends by replacing atomic compaction loops with a deterministic scheme using simdgroup scans and coalesced memory accesses. This change ensures byte-identical outputs across runs, improves performance by 20-45% on M5 Ultra hardware, and enables deterministic testing for Metal and ROCm. Redundant CUDA kernel resets were removed, and Vulkan/Metal changes were reduced or skipped.
The update qualifies calls to math functions like isnan and signbit with std:: to ensure compatibility with C++ standard library overloads, addressing issues on NetBSD 11.99.9/x86_64 with gcc 14.4.0. Additional C math function calls are standardized using std:: prefixes (e.g., std::fabs, std::copysign) instead of non-existent std::fabsf variants, improving accuracy by keeping computations in double precision. The change results in a minor behavioral difference with a relative error of at most 1.4e-7, well below test tolerances.
The server update addresses slot processing order by ensuring parent slots are visited before child slots, preventing delays caused by child tasks landing on lower slot IDs. This change ensures all child tasks join the same batch after being woken by their parent. Additionally, new test tags have been introduced for monitoring: __TEST_TAG_WAIT_PARENT_SLOT__ for child slot waits, __TEST_TAG_PROMPT_CACHE_RESTORE__ for prompt cache restores, and __TEST_TAG_PROMPT_CACHE_FULL_RESTORE__ when a cached prompt is fully contained in a new one based on token counts.
In our Cisco Catalyst C1300-24XTS review, we take the switch apart to see how it works and test it under full load to see if it holds up The post Cisco Catalyst C1300-24XTS Review A Neat 24-port 10GbE Switch appeared first on ServeTheHome.
A year ago, NVIDIA open sourced its Aerial wireless software, joining Sionna and a broader suite of open source tools for accelerating next-generation 6G network development. Together, they give researchers a foundation for testing ideas, comparing results and building on each other’s work — connecting simulation and AI training with experiments on real over-the-air wireless […]
The llama.cpp v1.21.70 release includes enhancements to the moe-cache system, introducing an LRFU/MQ-inspired hybrid cache with improved token handling, key computation, and decay calculations. Changes include adjusting the half-life parameter, increasing the number of classes to 8, refining score computation based on age, removing redundant branches, and improving documentation around unused slots and sorting. The release also features updated build binaries for multiple platforms including macOS, Linux, Windows, Android, and openEuler, with support for CUDA, Vulkan, ROCm, OpenVINO, SYCL, and other backends.
How vLLM migrated its CUDA build and compiled dependencies to the LibTorch Stable ABI, enabling compatible PyTorch upgrades without rebuilding vLLM.
Chris Alexiuk has a simple rule about open source AI: If you believe in it, be loud about it. “We should be shouting about this technology from the rooftops,” said Alexiuk, a Toronto-based senior product research engineer in deep learning at NVIDIA who specializes in making open large language models (LLMs) practical for working developers. […]
arXiv:2611.06397v1 Announce Type: new Abstract: Non-prefix KV-cache reuse encodes retrieved chunks independently and concatenates their caches. To find where a small repair budget should be spent, we vary the chunks around the answer-bearing one (the target) across three 7B-8B models and four datasets of dialogue memory, open-domain QA and API documentation. Reuse loses 19-100% of the context-dependent questions that full prefill answers, and it loses more when the other chunks are of the same kind as the target. Because the recoverable damage is spread over these other chunks, restoring a few of them recovers little, and the budget is better spent on the target. This works through contextual compatibility: recomputing the target against the reused cache beats inserting its full-prefill KV in all seven settings tested, although the recomputed KV stays far from the full-prefill KV. Question attention over reused caches, however, selects the target in at most 18.8% of development questions. A retriever finds it more often, and recomputing its top chunk after moving it next to the question significantly beats InfoFlow-style reordering at the same 5% budget in six of nine open-domain QA and API-documentation settings. On dialogue memory, though, recomputing chunks never significantly beats the token-level selector chosen for each setting.
The fix addresses intermittent CI failures in TOPK_MOE tests caused by near ties in logits, particularly with uniform random inputs and narrow-range gating functions like sqrt_softplus. The solution involves redrawing logits until group selection and top-k decisions have a relative margin of 1e-4 in double precision, and constructing logits by inverting the gating function using a grid-based approach to ensure consistent expert selection across backends.
One underperforming GPU can slow an entire distributed training job. The node may pass basic health checks and show normal utilization, leaving operators to...
arXiv:2611.05451v1 Announce Type: new Abstract: While discrete tokenization in Unified Multimodal Models (UMMs) establishes strong semantic reasoning, it introduces a strict quantization bottleneck that degrades fine-grained visual details, often resulting in structurally accurate but artifact-heavy images. We introduce FlowShift, a parameter-efficient post-training framework that bypasses this bottleneck by converting pre-trained discrete UMMs into continuous rectified-flow generators. Instead of discarding the backbone's semantic priors, FlowShift replaces discrete visual interfaces with continuous latent projections and applies LoRA to the attention layers. To efficiently boost visual quality without semantic drift, we propose Dual-Objective Velocity Vector Distillation (DOVD), which aligns the model with a high-fidelity continuous teacher and ground-truth continuous trajectories. This approach drastically improves perceptual quality while preserving prompt adherence and reasoning. When applied to leading discrete UMMs, FlowShift significantly improves COCO-30K FID and aesthetic scores, while retaining over 99.5% of multimodal understanding capabilities. Ablations confirm DOVD drives generation quality. Ultimately, FlowShift achieves high-fidelity continuous generation without sacrificing pre-trained semantic grounding.
As GPU clusters scale, operations span compute, network, hardware, and scheduling. Site reliability engineers (SREs) often have to examine multiple dashboards...
Shares of Kyndryl Holdings Inc. jumped 30% today after it reported better-than-expected earnings for the second quarter of its 2027 fiscal year. Kyndryl generated $3.9 billion in sales during the three months ended Sept. 30. That represents a 1.9% decrease from a year earlier, but the company still managed to top the consensus estimate by […] The post Kyndryl shares surge 30% on hyperscaler-related revenue growth appeared first on SiliconANGLE.
As artificial intelligence and accelerated computing continue to push the boundaries of what's possible, the latest NVIDIA CUDA release arrives to unlock...
arXiv:2611.04063v1 Announce Type: new Abstract: Transolver has recently gained widespread adoption for solving diverse physical problems on unstructured geometric meshes by leveraging its Physics-Attention mechanism, which seamlessly extends standard softmax attention to handle physical domains at scale. For time-dependent problems, existing literature relies on time-conditioned or autoregressive rollouts to preserve temporal causality while retaining Physics-Attention as the spatial backbone. However, a fundamental limitation of this formulation is its sequential execution model, wherein computational costs scale linearly with the temporal horizon, rendering standard Transolver inefficient for transient problems despite its exceptional spatial efficacy. This bottleneck has historically hindered its broader application in time-dependent physical modeling. To address this limitation, we introduce TB-Transolver (also referred to as Temporally Bundled Transolver), an efficient framework built on temporal bundling that scales Transolver for time-dependent PDEs. Furthermore, we propose \emph{spatiotemporal coherence} (${\rm C(K)}$) as a general metric to systematically quantify snapshot correlation within a temporal bundle, enabling the \emph{a priori} selection of optimal bundle widths using training data alone, thereby avoiding expensive grid searches. We demonstrate the computational efficacy of TB-Transolver across four physically distinct domains, namely, plasticity, fluid dynamics, reaction-diffusion, and global weather forecasting; achieving training speedups of up to $29\times$ over conventional autoregressive and time-conditioned baselines. Finally, these computational gains are further amplified through distributed multi-node and multi-GPU parallelism, yielding aggregated speedups of up to $45.6\times$ at optimal bundle widths for systems exhibiting high spatiotemporal coherence compared to single-GPU implementations.
