đ WELCOME TO METAMESH.BIZ +++ US fabs throwing $43B at chips by 2028 while OpenAI somehow got GPT-20B running on your phone (the compute moat just became a puddle) +++ Security researchers can't agree if AI will kill us all but at least someone built 99.9% accurate OCR so the paperwork will be pristine +++ Sora 2 already degrading like a JPEG saved too many times (baby dragons on Sunset Boulevard deserve better) +++ THE REVOLUTION WILL BE QUANTIZED, PHONE-OPTIMIZED, AND STILL SOMEHOW NEED MORE VRAM +++ đ âĸ
đ WELCOME TO METAMESH.BIZ +++ US fabs throwing $43B at chips by 2028 while OpenAI somehow got GPT-20B running on your phone (the compute moat just became a puddle) +++ Security researchers can't agree if AI will kill us all but at least someone built 99.9% accurate OCR so the paperwork will be pristine +++ Sora 2 already degrading like a JPEG saved too many times (baby dragons on Sunset Boulevard deserve better) +++ THE REVOLUTION WILL BE QUANTIZED, PHONE-OPTIMIZED, AND STILL SOMEHOW NEED MORE VRAM +++ đ âĸ
"Hi LocalLlama community. I present an LLM inference throughput benchmark for RTX4090 / RTX5090 / PRO6000 GPUs based on vllm serving and **vllm bench serve** client benchmarking tool.
Full article on Medium
[Non-med..."
đŦ Reddit Discussion: 18 comments
đ MID OR MIXED
đ¯ GPU performance âĸ Training and inference âĸ Parallelism and bottlenecks
đŦ "6000 Pro is one of the best 'deals' in GPUs that NVIDIA has shipped in a long time"
âĸ "It's worth tweaking all the knobs to figure out which set of tradeoffs best fits your specific workload!"
via Arxivđ¤ Shangqing Tu, Yaxuan Li, Yushi Bai et al.đ 2025-10-09
⥠Score: 7.8
"Parallel scaling has emerged as a powerful paradigm to enhance reasoning
capabilities in large language models (LLMs) by generating multiple
Chain-of-Thought (CoT) traces simultaneously. However, this approach introduces
significant computational inefficiency due to inter-trace redundancy -- our
ana..."
via Arxivđ¤ Hengrui Zhang, Pratyush Patel, August Ning et al.đ 2025-10-09
⥠Score: 7.6
"Large Language Models (LLMs) have gained popularity in recent years, driving
up the demand for inference. LLM inference is composed of two phases with
distinct characteristics: a compute-bound prefill phase followed by a
memory-bound decode phase. To efficiently serve LLMs, prior work proposes
prefi..."
+++ GPT-OSS 20B successfully runs locally on mobile hardware, proving that model optimization has come far enough to make your phone both smarter and hotter. +++
"I am looking for a few people to test TraceML, an open-source tool that shows GPU/CPU/memory usage live during training. It is for spotting CUDA OOMs and inefficiency.
It works for single-GPU fine-tuning and tracks activation + gradient peaks, per-layer memory, and step timings (forward/backward/o..."
đ¯ Tech industry hype and unsustainability âĸ AI ecosystem financial viability âĸ Potential for innovative products
đŦ "the tech industry has been in hot water since at least 2018"
âĸ "OpenAI and the rest of the AI ecosystem will need a financial miracle to stay afloat"
via Arxivđ¤ Qin Liu, Jacob Dineen, Yuxi Huang et al.đ 2025-10-09
⥠Score: 7.0
"Benchmarks are central to measuring the capabilities of large language models
and guiding model development, yet widespread data leakage from pretraining
corpora undermines their validity. Models can match memorized content rather
than demonstrate true generalization, which inflates scores, distorts..."
"Prompt 1:
Chasing the baby dragon that is flying at street level along the Sunset Boulevard at sundown. Cameraman is riding on a bike
Prompt 2:
The scene is a first-person POV of a busy crosswalk, with vehicles stalled at a red light on Sunset Boulevard. The same baby dragon playfully hops across..."
đŦ Reddit Discussion: 21 comments
đ MID OR MIXED
đ¯ Model Inconsistency âĸ Prompt Comparison âĸ Backend Changes
đŦ "This is normal. In backend they do lot of re-routing and you can never be sure it's the same model."
âĸ "They probably quantized it into 2 bits while re-routing requests to squeeze more money out of their customers!"
đ POLICY
China bans TechInsights after Huawei report
2x SOURCES đđ 2025-10-10
⥠Score: 7.0
+++ Chip analysis firm gets blacklisted for documenting Huawei's Ascend AI chips, proving that reverse engineering reports have consequences when you're good at it. +++
via Arxivđ¤ Tajamul Ashraf, Umair Nawaz, Abdelrahman M. Shaker et al.đ 2025-10-09
⥠Score: 6.8
"Vision language models (VLMs) are increasingly deployed as controllers with
access to external tools for complex reasoning and decision-making, yet their
effectiveness remains limited by the scarcity of high-quality multimodal
trajectories and the cost of manual annotation. We address this challenge..."
via Arxivđ¤ Zhen Zhu, Yiming Gong, Yao Xiao et al.đ 2025-10-09
⥠Score: 6.6
"How can we teach large multimodal models (LMMs) new skills without erasing
prior abilities? We study sequential fine-tuning on five target skills while
monitoring general ability on eight held-out benchmarks across three model
families. We observe that apparent "forgetting" on held-out tasks after n..."
via Arxivđ¤ Kai Zhang, Xiangchao Chen, Bo Liu et al.đ 2025-10-09
⥠Score: 6.6
"A long-term goal of language agents is to learn and improve through their own
experience, ultimately outperforming humans in complex, real-world tasks.
However, training agents from experience data with reinforcement learning
remains difficult in many environments, which either lack verifiable rewar..."
via Arxivđ¤ Rocktim Jyoti Das, Harsh Singh, Diana Turmakhan et al.đ 2025-10-09
⥠Score: 6.5
"Scaling data and models has played a pivotal role in the remarkable progress
of computer vision and language. Inspired by these domains, recent efforts in
robotics have similarly focused on scaling both data and model size to develop
more generalizable and robust policies. However, unlike vision and..."
"Reinforcement Learning with Verifiable Rewards (RLVR), which uses simple
binary feedback to post-train large language models, has shown significant
empirical success. However, a principled understanding of why it works has been
lacking. This paper builds a theoretical foundation for RLVR by analyzin..."
via Arxivđ¤ Hongyu Li, Lingfeng Sun, Yafei Hu et al.đ 2025-10-09
⥠Score: 6.3
"Enabling robots to execute novel manipulation tasks zero-shot is a central
goal in robotics. Most existing methods assume in-distribution tasks or rely on
fine-tuning with embodiment-matched data, limiting transfer across platforms.
We present NovaFlow, an autonomous manipulation framework that conv..."
via Arxivđ¤ Jiayun Luo, Wan-Cyuan Fan, Lyuyang Wang et al.đ 2025-10-09
⥠Score: 6.3
"Large Vision Language Models (LVLMs) have recently emerged as powerful
architectures capable of understanding and reasoning over both visual and
textual information. These models typically rely on two key components: a
Vision Transformer (ViT) and a Large Language Model (LLM). ViT encodes visual
con..."
via Arxivđ¤ Zilin Kang, Chonghua Liao, Tingqiang Xu et al.đ 2025-10-09
⥠Score: 6.1
"We propose ERA, a new paradigm that constrains the sampling entropy above
given thresholds by applying specially designed activations to the outputs of
models. Our approach demonstrates broad effectiveness across different domains:
1) for large language models(LLMs), boosting the AIME 2025 score for..."
via Arxivđ¤ Yuanjun Dai, Keqiang He, An Wangđ 2025-10-09
⥠Score: 6.1
"Existing batch size selection approaches in distributed machine learning rely
on static allocation or simplistic heuristics that fail to adapt to
heterogeneous, dynamic computing environments. We present DYNAMIX, a
reinforcement learning framework that formulates batch size optimization as a
sequent..."