NVIDIA

After TSMC manufactured its chip, Etched disclosed $1B in contracted orders for full inference systems—not just chips. Total funding now stands at $800M, including an unannounced $500M round. The pitch: faster, cheaper, more power-efficient inference for frontier models, which is the biggest cost bottleneck for AI companies serving users at scale. The post doesn't name specific customers, disclose performance benchmarks, or give a delivery timeline, so I'd discount the $1B figure until we see actual shipments.

Claude Science is an AI workbench for researchers, now in beta for Pro, Max, Team, and Enterprise users. It combines literature search, data analysis, figure generation, and manuscript editing in one session, with over 60 pre-configured skills and connectors for genomics, single-cell, proteomics, cheminformatics, and more. Every output includes auditable code, environment, and chat history for reproducibility. Compute runs locally on macOS or Linux, or on your lab's HPC cluster via SSH, with on-demand GPU scaling through Modal. The post does not disclose beta pricing or a general-availability timeline.

SemiAnalysis reports the original 4-die Rubin Ultra announced at GTC 2026 has been canceled after just 3 months due to manufacturing execution issues. The new Rubin Ultra is half the size with roughly half the performance. The post doesn't spell out which manufacturing steps failed or the new timeline.

NVIDIA's ENPIRE lets physical robots self-improve through trial and error like coding agents, hitting 99% on tasks like GPU insertion and zip-tie cutting. The catch: auto-evaluation and auto-reset still break on harder tasks. Tencent open-sourced ARGUS, an always-on tracing system for 10k+ GPU training clusters, already battle-tested for six months. A separate law paper points out that top minds badly misjudged nuclear fission and the internet—today's AI hot takes will likely age just as poorly.

OpenAI revealed Jalapeño, a custom inference chip built with Broadcom, joining Google, Apple, and SpaceX in reducing single-supplier dependence on Nvidia. The move is a hedge, not a clean break—more control and workload-specific performance, similar to Apple's shift from Intel. The same podcast episode covers Groq's $650M raise after Nvidia poached its top talent, and AI agents entering loops that Claude Code creator Boris Cherny calls as big a step as the jump from source code to agents.

LMSYS and NVIDIA introduced two dispatch-time load balancers in SGLang for DeepEP MoE inference. Waterfill handles the shared expert by sending it to the least-loaded GPU; it boosts throughput by 1.48–4.66% on DeepSeek V3/R1 and lifts DeepSeek V4 from 49,253 tok/s to 51,677 tok/s (+4.92%). LPLB uses linear programming to route tokens across redundant expert replicas, adding 0.84–7.34% throughput on the same benchmarks. Both methods preserve model accuracy. The post reports throughput gains but does not disclose latency impact.

Qualcomm set an aggressive target at its investor day: $15B in annual data center chip revenue by 2029. That's larger than its entire automotive and IoT segments combined in fiscal 2025. The bet is on AI inference chips, pivoting from training to inference and competing with Nvidia and Broadcom. The post doesn't disclose current data center revenue, but the base is clearly small. Going from near-zero to $15B in five years is a tight timeline.

OpenAI unveiled Jalapeño, its first custom chip built with Broadcom for ChatGPT inference. The chip is in mass production and already deploying on OpenAI's own servers, aiming to cut reliance on Nvidia and control costs. It only handles inference for now; a training chip is still in development. The post does not disclose performance, power, or cost figures.

SK Hynix plans a $29bn US IPO, the largest overseas listing by a Korean company. The capital will mainly expand HBM memory production for AI chip customers like Nvidia. The post doesn't disclose the timeline or pricing yet. The direction is clear: the AI arms race keeps pushing capital upstream.

AI chip startup Groq confirmed a $650M funding round and is rebuilding its executive team after Nvidia spent roughly $20B to hire away most of Groq's staff without acquiring the company. CEO Jonathan Ross says Groq is now leaning into a neocloud model—running inference services on its own chips instead of just selling hardware. New hires include a chief product officer and a VP of engineering, both from outside the group that left for Nvidia. The post does not disclose the round's valuation or investors.

SpaceX's Colossus 2 data center near Memphis landed its third major AI compute customer. Open source lab Reflection AI will pay $150 million per month starting July 1, 2026 through 2029 for immediate access to Nvidia GB300 chips. The deal follows earlier contracts with Anthropic ($1.25B/month) and Google ($920M/month). The post doesn't disclose what models Reflection AI plans to train or its funding sources.

Nvidia's AI boom is making SK Hynix, Samsung, and Micron extremely rich. The most advanced memory chips come only from these three—none from China. Huang wrote "Please produce more :)" on a Hynix wafer at Computex because supply can't keep up. Memory prices more than doubled this year. Samsung and SK Hynix made South Korea the first country outside the US with two trillion-dollar companies. US tariffs and tech restrictions locked China out of this boom more effectively than subsidies ever did. The supply chain now sits almost entirely in Taiwan and South Korea, two geopolitical flashpoints.

Subquadratic released the model card for SubQ 1.1 Small. It replaces quadratic dense attention with Subquadratic Sparse Attention (SSA) that routes based on content relevance, scaling linearly with context length. At 1M tokens, SSA uses 64.5x less compute than dense attention and runs 56x faster than FlashAttention-2. The model scores near-perfect on needle-in-a-haystack from 1M to 12M tokens and 99.12% on RULER at 128K. General reasoning holds: GPQA Diamond 85.4%, LiveCodeBench pass@4 89.7%, AutomationBench Finance 13%. Training started from an open-weight frontier model, replaced attention with SSA, then ran staged context extension up to 2M and ~1T tokens of continued pretraining on books, documents, and repo-scale code. The post does not name the base model. SubQ 1.1 Small is deploying with select design partners; a broader lineup from 2M to 12M tokens is planned later this year.

Apple is bringing NVIDIA's confidential computing into Private Cloud Compute, running AI inference inside encrypted GPU environments. The setup uses H100 GPUs and Hopper architecture with hardware-level trusted execution environments, so data stays encrypted during processing and even the cloud provider can't access it. Apple previously ran private cloud inference only on its own silicon; this deal signals a shift of some workloads to NVIDIA while keeping the same security isolation. The post doesn't give a launch date or scale numbers, but confirms deployment will start in Apple's own data centers.

Elon Musk detailed SpaceX’s AI1 orbital AI data center satellite plan, with 150 kW peak power per satellite, about 120 kW sustained compute power, and 6-8 ms round-trip latency at 600-800 km low Earth orbit.

The experiment used five models from OpenAI, NVIDIA, OpenBMB, and a self-fine-tuned 500M-parameter model to drive market agents; three interventions failed to reproduce the price crash, and the crash was created only by overriding prices during settlement.

The author tested Qwen3.6-27B on an RTX 5090 with DFlash plus KV cache compression, reaching up to 3.26x speedup; q4_0/turbo4 delivered 3.18x speedup with only +0.02% PPL on WikiText-2.

A Reddit user benchmarked qwen3.6-27b-autoround-int4 on 1x3090 versus 2x3090. Narrative decode rose from 53 TPS to 94 TPS, and code decode rose from 62 TPS to 120 TPS, under no NVLink, 8x/8x PCIe, P2P automatically enabled, tensor parallelism set to 2, and different KV-cache settings.

Nvidia and SK Hynix signed a multi-year pact to co-design future generations of memory chips for AI applications; the RSS snippet does not disclose product specifications, production timelines, or financial terms.

Thousand Token Wood v2 uses four small models from different labs to drive agents in a financial simulation game, with vLLM 0.22.1’s CUDA toolkit dependency identified as the main serving friction, while a fine-tuned 0.5B Qwen reached 0% self-trading and 100% valid quotes.