One GPU, 1M context, Day-0 ready. Big props to the vLLM team for the seamless integration!👍
Try Qwen3.8-27B on vLLM: @vllm_project
https://t.co/KDEzdWwimc
arXiv cs.AI
TIER_1English(EN)·Joyjit Kundu, Ben Stoffelen, Kaili Wang, Peter Vrancx, Ludovic Denoyer·
arXiv:2608.17071v1 Announce Type: new Abstract: We present KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads. Strategy-specialized agents run in parallel and coordinate through conclusions-only shared memory, a deterministic …
arXiv:2608.17379v1 Announce Type: cross Abstract: We introduce PTXBench, a benchmark for evaluating and adapting large language models (LLMs) to use architecture-specific PTX for GPU kernel optimization. PTXBench measures functional correctness, whether selected target instructio…
We introduce PTXBench, a benchmark for evaluating and adapting large language models (LLMs) to use architecture-specific PTX for GPU kernel optimization. PTXBench measures functional correctness, whether selected target instructions execute at runtime, and speedup over frontier l…
PTXBench evaluates large language models on architecture-specific GPU kernel optimization, revealing uneven success and performance gaps that supervised fine-tuning only partially addresses.
We present KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads. Strategy-specialized agents run in parallel and coordinate through conclusions-only shared memory, a deterministic benchmark guard, and read-only cross-agent state…
arXiv cs.CV
TIER_1English(EN)·Yutaro Oguri, Mai Nishimura, Yusuke Matsui·
arXiv:2508.15436v2 Announce Type: replace-cross Abstract: We propose a $\textbf{P}$latform for $\textbf{L}$ayout-$\textbf{A}$ware $\textbf{S}$earch and $\textbf{M}$emory $\textbf{A}$rrangement ($\textbf{PLASMA}$), a unified evaluation framework for graph-based Approximate Nearest…
Data Center Knowledge
TIER_1English(EN)·Pam Baker·
Distributed computing is getting a new spin. A growing crop of pilots is paying homeowners to host GPU capacity via wall-mounted appliances. Can residential nodes deliver the speed, reliability, security, and scale?
Hacker News — AI stories ≥50 points
TIER_1English(EN)·Jimmc414·
<p>When people first hear multiple GPUs, it’s easy to think:</p> <p>More GPUs = faster LLM.</p> <p>But that’s not always the case.</p> <p>The real question is:</p> <p>Why do we need multiple GPUs in the first place?</p> <p>There are mainly two problems:</p> <p>📌 The model fits on…
Wiwynn's NVIDIA SCADA prototype puts GPUs closer to storage, aiming to cut CPU I/O overhead in petabyte-scale AI racks. A useful reminder that AI performance isn't just about accel # tech # technology # ai # storage # nvidia # datacenter https:// techshowup.com/News/Article/01 a0…
<p>Qwen3.8-2.4T-A95B is a 2.4-trillion-parameter Mixture-of-Experts model with roughly 95B parameters active for each token. If you're planning to self-host it, the first thing to know is that this is a genuinely large distributed model: even the low-precision checkpoints are mea…
dev.to — LLM tag
TIER_1English(EN)·Devanshu Biswas·
<p>A language model does not write an answer in one shot. It runs a full forward pass to produce one token, appends that token to its own input, and runs again. A 300-token reply costs 300 sequential passes through billions of parameters — and nobody, including the model, knows i…
<p>Here is the short version: the model weights are the <em>smallest</em> GPU-memory surprise you'll hit. A 7B model in FP16 needs about 14GB just for weights, but the KV cache — the per-request memory that grows with context length and batch size — is what actually decides wheth…
dev.to — LLM tag
TIER_1English(EN)·ARSHIYA Sohrevardi·
<p>Running large language models locally often demands expensive hardware with high VRAM. However, for specialized tasks like offline Q&A and knowledge retrieval, a lightweight, highly optimized small language model (SLM) can deliver incredible speed and efficiency without br…
Qwen3.8-27B now runs efficiently on consumer-grade graphics processors, expanding the accessibility of large language models for everyday users # AI . # AINews
NVIDIA quantisiert Kimi-K3 (2.8T Parameter MoE, multimodal, 1M-Token-Kontext) auf NVFP4 fuer vLLM-Inferenz auf 8 Blackwell-B300-GPUs. Benchmarks wie GPQA Diamond (0.9321 vs 0.9277) zeigen kaum Verluste gegenueber dem Original. https:// huggingface.co/nvidia/Kimi-K3- NVFP4 # KI # …
