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4. 3rd & 4th line between Sason - Jharsuguda will strengthen Odisha's economy, logistics efficiency & enable faster train movement on Howrah-Mumbai route. 🛤️ Cost: ₹1,181 Cr. 🛤️ Length: 35 km route length; 87 km track length 🛤️ Traffic: 29 million tons cargo; more mail/exp/semi-high speed trains

44,660 Aufrufe • vor 1 Jahr •via X (Twitter)

9 Kommentare

Profilbild von Ashwini Vaishnaw
Ashwini Vaishnawvor 1 Jahr

4 major railway projects approved by Cabinet with total cost of ₹18,658 Cr. 📍Covering 15 districts in 3 states 🛤️ Adding 1,247 km 🧵👇🏻

Profilbild von Ashwini Vaishnaw
Ashwini Vaishnawvor 1 Jahr

1. 5th and 6th line between Kharsia – Naya Raipur – Parmalkasa will provide direct connectivity to new areas such as Baloda Bazar, enabling new industries & cement plants. 🛤️ Cost: ₹8,741 Cr. 🛤️ Length: 278 km route length; 615 km track length 🛤️ Traffic: 21-38 million tons cargo; 8 mail/exp/semi-high speed trains

Profilbild von Ashwini Vaishnaw
Ashwini Vaishnawvor 1 Jahr

2. Doubling of Gondia – Balharshah line in Maharashtra will enhance passenger and cargo movement between the Northern and Southern states of India, boosting tourism & economic activity in the region. 🛤️ Cost: ₹4,819 Cr. 🛤️ Length: 240 km route length; 268 km track length 🛤️ Traffic: Additional capacity for running more cargo & mail/express/passenger trains

Profilbild von Ashwini Vaishnaw
Ashwini Vaishnawvor 1 Jahr

3. Jarapada - Sambalpur 3rd and 4th line will increase economic activities in Talcher-Angul area. It will further enable running of more passenger and express trains in the region. 🛤️ Cost: 3,917 Cr. 🛤️ Length: 127 km route length; 277 km track length 🛤️ Traffic: 27 million tons additional cargo; more mail/exp/semi-high speed trains

Profilbild von Ashwini Vaishnaw
Ashwini Vaishnawvor 1 Jahr

5. The projects are result of PM Gati-Shakti National Master Plan for multi-modal connectivity. Aims to: ✅ Enable faster passenger & goods transport ✅ Lower logistic costs ✅ Save 95 Cr. litres of oil ✅ Cut 477 Cr. kg CO2 emission every year (equiv. to planting 19 Cr. trees!)

Profilbild von Square Deal Recordings & Supplies
Square Deal Recordings & Suppliesvor 1 Jahr

🚂✨ Check out our Railroad Crossing Keychain! Perfect for train lovers and collectors alike. 🚦 🛤️🔑 Get yours today and stay on track in style. #RailroadCrossing #Keychain #TrainLovers #Collectibles

Profilbild von ଜନବାର୍ତ୍ତା
ଜନବାର୍ତ୍ତାvor 1 Jahr

Thanks ashwiniji.. You are invited to sambalpur... We are eagerly waiting.

Profilbild von R.K.Verma 🌊​🇷​​🇫​
R.K.Verma 🌊​🇷​​🇫​vor 1 Jahr

@RailMinIndia @AshwiniVaishnaw @RavneetBittu @IR_CRB @IR_EDCHG @comnrly @NWRailways @GMNWRailway @CptmNwr @drmbikaner @SrDCMBKN @SRDOMBKN श्रीधाम एक्सप्रेस और ब्रह्मपुत्र मेल का विस्तार वाया रेवाडी,सादुलपुर, हनुमानगढ़ तक किया जाये इस क्षेत्र कि सबसे महत्वपूर्ण मांग है 🙏🙏🙏

Profilbild von ꜱʜɪᴠ ᴮᴴᵁ
ꜱʜɪᴠ ᴮᴴᵁvor 1 Jahr

Great intent, best wishes

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P C Mohan

64,510 Aufrufe • vor 1 Jahr

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30,092 Aufrufe • vor 27 Tagen

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16,998 Aufrufe • vor 4 Monaten

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Rebel News

12,845 Aufrufe • vor 4 Monaten

$AMD $5 Trillion MC Is Inevitable Long Term👑 This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of 💩 or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same → make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!

Mike

102,223 Aufrufe • vor 7 Monaten