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$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!

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📢3/26(木)アップデートプレビュー 🎉3月26日に予定されているアップデート情報を再度ご案内！ 快適なゲーム利用のためにメンテナンス時間をチェックしてくださいね🕗 🛠️メンテナンス時間： 3月26日(木)8:50～12:30 🔗詳細を見る ✨主要アップデート内容 1. シーズンイベント「The Erosion」アップデート 2. 新キャラクター「終焉の夜 マモニル」アップデート 3. 新スペシャルスキン「ライジング・スター ヘレナ」追加 4. 「コードネームO エリーゼ」後日談アップデート 今回のアップデートもお楽しみに！ #ブラウンダスト2# #アップデートプレビュー#

📢 March 25th Update Preview Heads up! Here’s a sneak peek at what’s coming with the March 25th update! Please take note of the maintenance time to avoid any gameplay interruptions. 🛠️Maintenance Schedule: March 25th, 11:50 PM - March 26th, 03:30 PM (UTC). (3 hr 40 min) 🔗 ✨ What’s New: 1. Season Event 'The Erosion' Update 2. [NEW] Night of Death Mamonir 3. [NEW/Special Skin] Rising Star Helena 4. [Follow-up] Code Name O Elise Thanks for your patience and get ready for some awesome new content! If you’re excited for this update, don’t forget to hit that like button! #BrownDust2# #Update# #Sneakpeek#

📢3/26(목) 업데이트 미리보기 🎉3월 26일 진행 예정인 업데이트 소식을 다시 한번 안내드립니다! 쾌적한 게임 이용을 위해 점검 시간을 꼭 체크해 주세요. 🕗 🛠️ 점검 시간 : 2026년 3월 26일(목) 오전 8:50 ~ 오후 12:30 🔗 자세히 보기 : ✨ 주요 업데이트 내용 1. 시즌 이벤트 'The Erosion' 업데이트 2. 신규 캐릭터 '죽음의 밤 마모니르' 업데이트 3. 신규 스페셜 스킨 '라이징 스타 헬레나' 업데이트 4. '코드네임 O 엘리제' 후속 스토리 업데이트 항상 기다려주시는 여러분께 감사드리며, 이번 업데이트도 많은 기대 부탁드립니다! 🚀 #브라운더스트2# #업데이트# #미리보기#

🎥BrownDust2 ｜The Erosion PV The King of the Undead, Mamonir is here! Embark on an epic quest to take back Taros alongside the ultimate tactical mastermind. If you want to stay up to date with BrownDust2 news, don’t forget to follow and like 💖! #BrownDust2#

🎥브라운더스트2｜The Erosion PV 언데드의 왕 마모니르가 옵니다! 천재 전략가인 마모니르와 함께 타로스를 되찾기 위한 여정을 함께하세요. 브라운더스트2의 소식을 빠르게 확인하고 싶다면 팔로우와 좋아요💖 부탁드립니다! #브라운더스트2# #Erosion# #마모니르#

3月26日(木)メンテナンスにつきまして、ご案内いたします。 ご利用の際のご参考として頂ければと存じます。 ■ 日程： 3月26日(木)8:50～12:30 ■ 内容 🐙 シーズンイベント「The Erosion」開始 イベント開催期間： 3月26日(木)メンテナンス後～4月9日(木)メンテナンス前まで 巨大魔物「コンプレックス(火)」の狩猟準備期間がオープンされます。 狩猟準備期間： 3月26日(木)メンテナンス後～4月2日(木)8:59まで 狩猟期間： 4月2日(木)9:00～4月8日(水)23:59まで コンプレックス 🐙コードネームO エリーゼ 絆の客 続編ストーリーの追加 🐙 絆の客のフルボイス追加 一部キャラクターのボイスが追加されます。 詳しくは公式サイトのお知らせを御覧ください。 🐙 ヘレナのスペシャルスキン追加 B級アイドル ヘレナのコスチュームの外見を変更できるスペシャルスキン「ライジング・スター ヘレナ」が入手できます。 🐙ギルドレイドシステムの変更 1) 侵略防衛戦の削除 ４月９日からギルドレイドの侵略防衛戦が削除され、ボス防衛戦が7日間行われるように変更されます。 2) ボス防衛戦不参加時の報酬獲得方法の変更 3) ランスロットゲージの改善 🐙ストーリーおよびキャラクター設定の一部調整（2次） 一部ストーリー及びキャラクター設定の調整を行います。 詳しくは公式サイトのお知らせを御覧ください。 🐙 その他のご案内 ・釣りマルチプレイ時、最大人数が乗船すると自動的に出航するよう変更されます。 ・ロード時間の改善のため、設定＞グラフィック＞ゲーム開始画面を「ホーム画面」選択しゲームを開始する際に、ホーム画面への初進入時にカセットロードが削除されるよう変更されます。 ・嫉妬の夜 レヴィアのプロフィール内容が一部修正されます。 ・3月7日のプロデューサーレターにて、3月26日に最適化パッチを実施するとご案内いたしましたが、メモリー使用量減少作業と安定性の検証に時間がかかるため、4月中反映に変更となりました。約束していた日程が変わってしまった点、ユーザーの皆様には何卒ご理解いただけますようお願い申し上げます。関連パッチの実施時には別途お知らせいたします。 ・ドイツのIPから接続した場合、一部のコスチュームの立ち絵が、元のコスチュームデザインとは異なるデザインに変更されて表示されます。 ・ファンタジア・スクエア内の「銅像叩き」コンテンツの終了期間が変更されます。 既存: 3月26日メンテナンス前まで 変更: 4月9日メンテナンス前まで ・以前3月14日に告知でご案内したゴールデンコロシアムの戦闘勝利ミッションの報酬が遡り適用されます。 ■ピックアップおよび新規キャラクターのご案内 コスチューム「終焉の夜 マモニル」と専用装備「グリム・サージ」がピックアップに登場します。 ピックアップ期間： 3月26日(木)メンテナンス後～4月9日(木)メンテナンス前まで ※終焉の夜 マモニルコスチュームは2026年4月9日(木)メンテナンス後、希望の粉商店及び選別ガチャに追加されます。 [復刻] コスチューム「B級アイドル ヘレナ」と専用装備「トップアイドル」がピックアップに登場します。 ピックアップ期間： 3月26日(木)メンテナンス後～4月9日(木)メンテナンス前まで ※B級アイドル ヘレナコスチュームは2026年4月9日(木)メンテナンス後、希望の粉商店に追加されます。 ■その他修正 その他、不具合の修正を予定しております。 アップデートの詳細につきましては、下記をご確認ください。 アップデートコンテンツをお楽しみ下さい✨️ #ブラウンダスト2#