Herr Greenrush (e/acc)

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Herr Greenrush (e/acc)

Herr Greenrush (e/acc)

@HerrGreenrush

Coder and GPU-poor AI alchemist. News and thoughts about technology. English/Deutsch.

Switzerland Beigetreten Ekim 2017
2.9K Folgt687 Follower
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Zephyr
Zephyr@zephyr_z9·
Extremely high alpha podcast
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Jukan
Jukan@jukan05·
TrendForce Raises Q1/Q2 2026 DRAM & NAND Price Forecasts Q1 2026 - PC DRAM Blended ASP: revised up from +50~55% to +110~115% - Server DRAM Blended ASP: revised up from +60~65% to +93~98% - Mobile DRAM Blended ASP: revised up from +45~50% to +58~63% - Overall DRAM Blended ASP: revised up from +55~60% to +93~98% - eSSD prices: revised up from +33~38% to +75~80% TLC/QLC NAND prices: revised up from +30~35% to +75~80% - Overall NAND Blended ASP: revised up from +33~38% to +85~90% Q2 2026 - PC DRAM prices: revised up from +10~15% to +40~45% - Server DRAM prices: revised up from +10~15% to +43~48% - Mobile DRAM (LP5X) prices: revised up from +13~18% to +58~63% - eSSD prices: revised up from +15~20% to +68~73% - TLC/QLC NAND prices: revised up from +15~20% to +60~65% - Overall NAND Blended ASP: revised up from +18~23% to +70~75%
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Romain Huet
Romain Huet@romainhuet·
We just launched Codex use cases! It’s a gallery of practical examples across coding and non-coding tasks, with real ways to use Codex. One thing I really like: if you have the app, you can open the starter prompt for each use case directly in Codex! developers.openai.com/codex/use-cases
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Bloomberg
Bloomberg@business·
Physical Intelligence, a two-year-old AI robotics startup by ex-DeepMind staffers, is in talks to double its valuation to more than $11 billion bloomberg.com/news/articles/…
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Sam Altman
Sam Altman@sama·
The first steel beams went up this week at our Michigan Stargate site with Oracle and Related Digital
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SemiAnalysis
SemiAnalysis@SemiAnalysis_·
AI inference isn't a commodity. It's a managed experience. Labs that understand the interactivity lever operate at 60%+ margins. The rest race to zero. (5/5)
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Epoch AI
Epoch AI@EpochAIResearch·
The total memory bandwidth of AI chips shipped since 2022 has reached 70 million terabytes per second, growing 4.1x per year. That's around 300,000x more data per second than global internet traffic.
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Ben Bajarin
Ben Bajarin@BenBajarin·
From here on out, all silicon architecture designs will be designed with agentic AI in mind. Basically, all silicon becomes agentic workload-focused.
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Zephyr
Zephyr@zephyr_z9·
"HG Tech recently raised its projection of optical transceiver demand in China from 20 million in 2026 to 20 to 30 million. That's around 1/3 of global demand." Interesting
tphuang@tphuang

Glenn is framing this Reuters article correctly. The most important part to take-away from this article is its px as part of Atlas-350 & that ByteDance is ordering it. Since Reuters report of production # keep going up every time, I would seriously just ignore any # they provide. Remember all the stories of DeepSeek V4 coming out in a week? Where is it? There are already 7 Atlas-350 vendors during the launch last week & Reuters report its targeting shipment in 2H? This is entirely ludicrous. Now, I wouldn't compare Ascend-950PR to H200, since the former is a lower cost option using cheaper/lower end HBM designed for inference. PR - (Prefill & Recommendation). Given the lower yield of producing HBM3 type of HBM (stack 8 die on top of each other, if yield of 1 die 90% -> 8 dies yield 43%. If you layer 4 die instead, yield 66%). Take a look at current inference speed of Minimax or GLM on Macbooks using VRAM. You don't need HBM3 to run inference on these models. Reuters article also fundamentally doesn't understand what "premium version" is. Ascend-950DT (Decode & training) - hence the support for 2 TB/s interconnect speed, 4 TB/s 144GB HBM & connected together in Atlas-950 thru OXC/OCS - fully optical SuperNode that can be connected w/ 63 other ones to form 1 ZFLOP SuperCluster, enough for multiple trillion parameter training runs. There are many reason why HW is doing things this way. 950 die is clearly smaller than 910C due to lower yield of SMIC N+2 vs TSMC N7. Training is harder than inference from networking + software pov. See where optical network tech is now wrt Hisilicon & such. 8-layer HBM is harder than 4-layer HBM, so you start of by producing 4-layer HBM, which is more than sufficient for inference. Inference demand is also much higher than training due to the recent OpenClaw phenomenon. As for production #. since China is expected to have close to 100k wpm of 5-7nm capacity by EOY. Let's say 25k is used for AI chips. And you get 80 good die per wafer (+ 2 die per chip), do your own calculation. HG Tech recently raised its projection of optical transceiver demand in China from 20 million in 2026 to 20 to 30 million. That's around 1/3 of global demand. Again, do your own projections. Things shouldn't be difficult to figure out if you put your mind into it. And no, EUV is not a must. It's a good to have.

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AI at Meta
AI at Meta@AIatMeta·
We’re releasing SAM 3.1: a drop-in update to SAM 3 that introduces object multiplexing to significantly improve video processing efficiency without sacrificing accuracy. We’re sharing this update with the community to help make high-performance applications feasible on smaller, more accessible hardware. 🔗 Model Checkpoint: go.meta.me/8dd321 🔗 Codebase: go.meta.me/b0a9fb
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matthew sigel, recovering CFA
NVDA H100 rental prices hit 18-month high: $2.59/hour
matthew sigel, recovering CFA tweet media
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The AI Investor
The AI Investor@The_AI_Investor·
The fact that bad takes, or FUD, spread much faster on X than good ones says something about either the algorithm or human behavior. Take TurboQuant as an example. My post was early, but it got low engagement, while some other FUD posts got millions of views. I am not saying I should get higher engagement, but X should punish bad takes and low quality content instead of promoting it. @nikitabier
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Theo Bearman
Theo Bearman@theobearman·
Per the Fortune article: After being contacted by Fortune, the company acknowledged that is developing and testing with early access customers a new model that it said represented a “step change” in AI capabilities, with significantly better performance in “reasoning, coding, and cybersecurity” than prior Anthropic models. Regardless of when 'Mythos' is publicly released, I look forward to reading Anthropic's discussion "in a system card or elsewhere" of "how that model’s capabilities and propensities affect or change the analysis in the Risk Report" within the next 30 days.
Theo Bearman tweet media
prinz@deredleritt3r

Anthropic has been testing a new model called "Mythos" with certain customers: - a "step change" in AI capabilities, including "dramatically higher scores" in coding, academic reasoning and cybersecurity - "currently far ahead of any other AI model in cyber capabilities” - part of a new "Capybara" series of models, which are larger and more intelligent than Opus - more expensive to run than Opus; not yet ready for general release

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M1
M1@M1Astra·
Claude Mythos Blog Post Saved before it was taken down. m1astra-mythos.pages.dev
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Dan Nystedt
Dan Nystedt@dnystedt·
Micron inaugurated its new US$1.8 billion Tongluo fab campus in Taiwan on Thursday (3/26), media report, adding it expects volume shipments from the site to start in its fiscal year 2028 (CYQ3 2027) and will hire 1,000 new workers, raising its total in Taiwan to 15,000 by end-2026. 1/2 $MU #Powerchip #Taiwan #semiconductors #semiconductor
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Jukan
Jukan@jukan05·
* REUTERS: Chinese big tech companies, including ByteDance and Alibaba, are expected to place large-volume orders for Huawei’s 950PR. * REUTERS: Chinese big tech companies are reportedly satisfied that the 950PR offers better compatibility with Nvidia’s CUDA software ecosystem and faster response speeds compared with the 910C. * REUTERS: Huawei plans to ship about 750,000 units of the 950PR this year.
Jukan@jukan05

Wait, what? According to Reuters, the 950PR is said to come in two versions: one using DDR and the other using HBM.

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Jukan
Jukan@jukan05·
According to Korean media, purchasing departments at domestic semiconductor manufacturers such as Samsung and SK hynix are checking on a daily basis whether key materials such as helium can be procured and how prices are moving, as they make every effort to prevent production disruptions. Spot helium prices have already surged by more than 50%. Since these materials account for only a small portion of semiconductor production costs, they are not expected to have a direct impact on chip prices. However, the biggest concern is potential disruption to production. Accordingly, Samsung and SK hynix are said to be securing inventory by accepting market prices without resistance. (The Elec)
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Financial Times
The Pentagon has been blocked by a US court from punishing Anthropic over its refusal to allow unrestricted use of its technology in warfare, in a blow to the Trump administration in its row with the AI start-up. ft.trib.al/H73mt2F
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