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おはよう~~~！☀️ 本日朝劇です！ その後は弊学の文化祭のために #ニコニコ超会議2026# 行きます‼️ ニコ超での2回目の文化祭楽しみだーーー！ #ZEN大学# #展軸祭#

$AMD| The FOMO to buy @AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: @WSJ yesterday came out with an article that @OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from @AnthropicAI has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited @AnthropicAI is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why @OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

留意 $AMD 7月大涨的机会 AMD Advancing AI 2026，定于 7月22-23日，地点旧金山 Moscone Center（同步线上直播）。 这是 AMD 的年度旗舰 AI 大会，规格对标 NVIDIA GTC。 主要看点： •Lisa Su 发表主题演讲，另有生态合作伙伴、客户及开发者参与  •预计发布下一代 Instinct MI500 系列 AI 加速器，以及基于 Zen 7 架构的 Verano CPU  •去年大会发布了 MI350 加速器、Helios 机架，并预告了 EPYC Venice 系列  •整体围绕 AMD 从芯片到软件的全栈 AI 方案，覆盖构建、部署、规模化等议题  投资角度值得关注的点： MI500 的规格与 TSMC N2P 制程进展（能否正面竞争 Blackwell/Vera Rubin）、ROCm 软件生态更新、以及是否有新的 OEM/云厂商合作公告，都是市场关注焦点。

$INTC 与 $AMD 在AI PC浪潮下的处境对比： 英伟达携手微软、MediaTek及宏碁、戴尔、联想等OEM厂商共推Windows Arm PC（RTX Spark），直接冲击的是Intel赖以为生的三大护城河——x86生态、CPU性能与OEM渠道。Windows on Arm的Prism兼容层若持续成熟，Intel的x86锁定优势将加速瓦解，是此轮受伤最深的一方。AMD所受冲击相对有限，因其本身就具备强GPU与强APU基因——Strix Halo / Ryzen AI Max走的正是”CPU + GPU + 大内存池”路线，Ryzen AI Max+ 395更以16核Zen 5、TSMC 4nm、128GB统一内存（最高112GB可分配给GPU）构筑本地生成式AI能力，具备一定反击筹码；核心短板在于GPU软件生态与CUDA的差距短期难以弥合。

おはよ〜！念願の！ 📍#品川サウナ# LD 🧖🏻‍♀️🩷 サウナは2種類あって ZENでは個室で黙々と漢方の香りを全身に🧘🏻‍♀️ ホースで無限､､､🥹🚿 3セット目のKUUでは 3種類の香りと音楽と共に圧巻のアウフグースも🌪🌸 内気浴と外気浴どちらも良くて サービスも沢山で永遠にいられそうでした🥹 #サウナガトモ#

AIイラスト初心者向け『簡易背景テンプレ』第3弾：10選✨ ​テーマ：質感を極める「アーティスティック背景」 「ただの背景じゃ物足りない、高級感や独特の空気感を出したい」 ​→ 質感や光の粒子にこだわり、キャラの存在感を芸術的に引き立てます👍 ​💡 使い方ガイド まずは**【基本】をコピペ！ もっと密度や輝きを足して「魅せたい」時は、後ろに【応用】**を付け足してください✨ ---- ​① Floating Petals（舞い散る花吹雪） ​【基本】floating flower petals, soft breeze, natural light ​【応用】, swirling petals, depth of field, blurred foreground petals, floral scent atmosphere, sunlight filtering ​画面全体に華やかさを 👉 民族衣装やドレスなど、優雅で動きのあるポーズに ---- ​② Gold Leaf Art（和風・金箔） ​【基本】japanese gold leaf background, traditional washi texture ​【応用】, golden flakes, cracked gold foil, kintsugi style, elegant metallic sheen, luxury oriental aesthetic ​高級感あふれる和の芸術 👉 チーパオや和装を、豪華でアートな雰囲気に仕上げたい時に ---- ​③ Cyber Neon Grid（サイバー・グリッド） ​【基本】neon grid floor, digital horizon, synthwave sun ​【応用】, glowing wireframe, retro-futuristic, scanlines, holographic reflections, laser beams, 80s sci-fi style ​デジタルな仮想空間 👉 サイバー・チーパオやメカ系衣装の「ステージ」として最適 ---- ​④ Stained Glass Hall（ステンドグラスの回廊） ​【基本】stained glass background, colorful light refraction ​【応用】, kaleidoscopic shadows, church atmosphere, glowing mosaic, cinematic light beams, vibrant colors ​光と影の宝石箱 👉 ゴシック衣装や、幻想的な光をキャラに浴びせたい時に ---- ​⑤ Ancient Stone Ruin（古の石造遺跡） ​【基本】ancient stone ruins, overgrown ivy, mossy texture ​【応用】, crumbling pillars, sunlight through cracks, mysterious altar, weathered stone, fantasy adventure vibes ​歴史を感じる退廃美 👉 冒険者、北欧風狩人、部族風スタイルに物語性をプラス ---- ​⑥ Liquid Glitter（リキッド・グリッター） ​【基本】shimmering liquid background, sparkling bokeh, fluid motion ​【応用】, flowing silk texture, iridescent reflections, metallic ripples, magical glitter, luxury aesthetic ​ヌルテカした光の質感 👉 艶のあるバニーガールや、肌の質感を強調したい時に ---- ​⑦ Bamboo Grove（静寂の竹林） ​【基本】bamboo forest background, cool green shade, zen ​【応用】, tall bamboo stalks, soft fog, sun rays through leaves, serene atmosphere, traditional eastern style ​凛とした空気感 👉 巫女服や忍者のような、静かでクールな立ち姿に ---- ​⑧ Magma & Ember（マグマと火の粉） ​【基本】volcanic background, glowing embers, smoke ​【応用】, flowing lava, sparks flying, dark ash, intense heat haze, dramatic firelight, hellish aesthetic ​燃え上がる情熱と迫力 👉 戦闘美を感じる砂漠の暗殺者や、力強いポーズの演出に ---- ​⑨ Clockwork Gear（クロックワーク・ギア） ​【基本】giant clockwork background, brass gears, ticking ​【応用】, intricate mechanical parts, gold and copper tones, cogs and wheels, victorian steampunk, technical details ​緻密な造形美 👉 スチームパンクや、衣装にメカ要素があるキャラクターに ---- ​⑩ Minimalist Geometry（ミニマル幾何学） ​【基本】minimalist geometric background, flat colors, simple shapes ​【応用】, clean vector art style, balanced composition, bold shadows, modern graphic design, pop aesthetic ​雑誌の表紙のような洒落感 👉 ストリート浴衣や、キャラのシルエットを一番に目立たせたい時に ---- ​【さらに質感を高める！追加ワード集】 ​光の粒子を飛ばす sparkling dust motes, fireflies, floating light particles, glowing embers ​画面に奥行き（ボケ）を出す blurred foreground, extreme depth of field, focused on subject ​色の深みを出す monochrome, sepia tone, vibrant saturation, muted colors ---- ​第3弾は、背景自体がひとつの作品になるような「質感」をテーマにしました。 そのまま使うのも良いですが良かったら参考にしながらお気に入りの背景を作ってみてください✨

今天看AMD财报及电话会议能确认的几个关键点： 1、明确提到了服务器CPU的TAM（潜在市场总量）到2030年超过1200亿美元，苏妈原话是这么说的:“基于我们目前观察到的需求信号，以及智能体AI(AgenticAI)驱动的CPU计算需求的结构性增长，我们现在预计服务器CPU的潜在市场总量(TAM)将以每年超过 35% 的速度增长，到 2030年将超过1200亿美元。” 而25年11月分析师大会上当时预估时为约600亿美元（18%CAGR），现在是1200亿美金，35%CAGR 2、数据中心收入58亿美元首次超越英特尔：同比增长57%至58亿美元，其中服务器CPU收入同比增长超50%创历史新高。同期英特尔DCAI收入51亿美元、增速22%——AMD在数据中心领域的收入规模首次实现反超。 。从2017年Zen架构首次进入服务器市场到今天，AMD用了近10年时间完成了这个历史性的反超。 3、服务器CPU业务是最大亮点——连续第4个季度创收入记录，同比增长超50%。云和企业客户各增长超50%，EPYC驱动的云实例数同比增长近50%至超过1600个。5代EPYC Turin的爬坡和4代Genoa的持续出货共同推动了增长。苏妈特别指出，增长主要由出货量驱动而非提价，ASP的提升更多来自产品组合和核心数的增加。 4、服务器CPU的故事正变得越来越有说服力。TAM从600亿翻倍至1200亿本身就是一个值得停下来消化的数字。管理层的逻辑链很清晰：Agentic AI的普及→推理量爆发→每个agent需要CPU来编排和处理数据→CPU与GPU的配比从1:4提升到1:1甚至更高→CPU TAM倍增。Q2服务器CPU收入指引同比增长超70%，全年增长轨迹还在加速。 4月中的这篇推文，也有详细聊到CPU在Agent时代关键位置提振的底层逻辑，也给到个人认为利好的标的，今天都在爆发，连A股的海光信息都涨停了20%一根大阳线。 现在看CPU这个趋势还在加强