Originally published at twarx.com - read the full interactive version there.
Last Updated: June 22, 2026
NVIDIA just made the cold data center obsolete with an AI technology breakthrough — and the counterintuitive choice that did it is running coolant hotter than a hot tub.
On June 21, 2026, NVIDIA revealed that its Rubin generation AI technology infrastructure is the world's first to achieve 100% liquid cooling at up to 45°C (113°F), eliminating fans entirely and cutting water use to near zero. This is one of the most consequential AI technology shifts of the year because cooling has historically eaten up to 40% of data center electricity. After reading this, you'll understand the full thermal architecture, the dollar impact, and the deeper systems lesson hiding inside it.
NVIDIA's 45°C liquid-cooling architecture for the Rubin AI factory — every chip cooled by liquid in a closed loop with no fans. Source: NVIDIA Blog
Most AI infrastructure conversations are solving the wrong problem. Everyone obsesses over GPU count and raw FLOPs — that's the easy argument to make in a budget meeting. But the breakthrough NVIDIA just shipped isn't about compute. It's about coordinating heat, power, and water across an entire AI factory. That same blind spot — optimizing components while ignoring the system that connects them — is exactly what kills most production AI deployments. I call it the AI Coordination Gap, and this cooling story is the cleanest illustration of it I've ever seen. If you want the software-side parallel, our guide to orchestration walks the same line.
Coined Framework
The AI Coordination Gap
The AI Coordination Gap is the systemic failure that emerges when teams optimize individual components — a GPU, a model, an agent — while ignoring the orchestration layer that makes them work together. The biggest gains (and the biggest failures) in AI almost always live in the coordination, not the components.
Overview: What NVIDIA Announced and Why It's a Coordination Story
On June 21, 2026, NVIDIA's Josh Parker published "Hotter Than a Hot Tub: The 45°C Breakthrough to Cool AI's Biggest Machines". The headline fact: NVIDIA's newest AI servers run their cooling liquid at up to 45 degrees Celsius (113°F) — warmer than a hot tub, which sits at roughly 38–40°C. And that higher temperature is precisely what makes them more efficient.
The Rubin generation is the world's first NVIDIA AI infrastructure to achieve 100% liquid cooling: every chip, every networking component, cooled entirely by liquid in a closed loop with no fans anywhere in the system. The methodology is codified in the NVIDIA DSX AI factory reference design — a blueprint for designing, building, and operating the full AI factory infrastructure stack.
Why does temperature-up equal energy-down? Because the warmer your loop runs, the more of the year you can reject heat using outdoor dry coolers instead of energy-hungry mechanical chillers. Ali Heydari, director of data center cooling and infrastructure at NVIDIA, put it bluntly: "The NVIDIA DSX reference design for AI factories has zero water consumption — we have eliminated massive amounts of power usage and pretty much all water usage." Independent reporting from DataCenter Dynamics and the U.S. Department of Energy has long flagged cooling as the dominant efficiency lever, which is why this shift matters.
Here's where the coordination lens snaps into focus. The win isn't a better chip or a better radiator. It's the recognition that the data center ambient temperature, the chip thermal envelope, the coolant chemistry, and the facility's water loop are one coupled system. Optimize them together and you capture a 40% energy category and near-100% water savings. Optimize them separately — the historical default — and you leave all of that on the table. That's the AI Coordination Gap, applied to thermodynamics instead of agents. Researchers at Nature and the Lawrence Berkeley National Laboratory have made the same systems argument about data center efficiency for years.
45°C
Coolant inlet temperature in Rubin systems (113°F)
[NVIDIA Blog, 2026](https://blogs.nvidia.com/blog/liquid-cooling-ai-factories/)
40%
Share of data center electricity historically spent on cooling
[NVIDIA Blog, 2026](https://blogs.nvidia.com/blog/liquid-cooling-ai-factories/)
$4M+
Annual savings for a 50MW facility moving to liquid cooling
[NVIDIA Blog, 2026](https://blogs.nvidia.com/blog/liquid-cooling-ai-factories/)
2.6M gal
Water per MW/year for conventional cooling-tower systems — driven to near zero
[NVIDIA Blog, 2026](https://blogs.nvidia.com/blog/liquid-cooling-ai-factories/)
What Is It: The 45°C Liquid-Cooled AI Factory in Plain Language
If you've never set foot in a data center, here's the simplest version. A modern AI chip generates so much heat that traditional approaches — blasting cold air across it with fans — can no longer keep up. NVIDIA's solution is to pipe liquid directly onto the chip through a metal plate (a "cold plate"), absorb the heat at the source, and carry it away through a closed loop of pipes. No air required. No fans at all.
The surprising part: that liquid doesn't need to be cold. It enters the chip at 45°C and exits at roughly 55°C, having soaked up the heat load. The chip runs at full performance the entire time because the cold plates hold the silicon within its validated operating limits. As NVIDIA notes, "performance doesn't degrade" even with coolant entering the rack at 45°C.
The coolant itself is a specific recipe: 75% water and 25% propylene glycol. It flows from a coolant distribution unit (CDU) to the servers and back in a continuous closed-loop cycle — the same liquid recirculated indefinitely, so no new water is consumed to cool the chips.
For decades the industry believed a cold data center was an efficient one. NVIDIA just proved the opposite: the hotter you let your loop run, the less energy and water you burn keeping it cool.
The single biggest mental shift here is that room temperature no longer matters. NVIDIA states it directly: "warm summer air is fine — because nothing in the server depends on cool air. The liquid does all the work." That decoupling — server thermals from ambient air — is the entire ballgame.
The old model: cold aisles, hot aisles, and fans at 85+ decibels. The Rubin model: no fans, no cold aisles, liquid doing all the thermal work — a direct illustration of the AI Coordination Gap closing.
How It Works: The Thermal Architecture, Step by Step
Let's trace the heat from silicon to sky. This is where the coordination thinking becomes concrete — each stage hands off to the next, and a failure to coordinate any single handoff collapses the efficiency gains. The same logic underpins our work on multi-agent systems.
NVIDIA Rubin 45°C Closed-Loop Cooling Flow
1
**Cold Plate on Chip**
Coolant (75% water / 25% propylene glycol) enters the cold plate at 45°C, sitting directly on each processor and networking component. Heat is captured at the source — no air involved.
↓
2
**Heated Coolant Exit (~55°C)**
Having absorbed the chip's heat load, coolant exits at roughly 55°C. The silicon stays within validated operating limits the entire time — full performance, no throttling.
↓
3
**Coolant Distribution Unit (CDU)**
The CDU routes hot coolant out of the rack and returns cooled liquid back to the servers in a continuous closed loop. This is the orchestration layer of the thermal system.
↓
4
**Outdoor Dry Cooler**
Because the loop runs hot, outdoor dry coolers reject heat to ambient air for most of the year — no mechanical chillers, no evaporative water loss. Chillers fire only ~1% of the year in some climates.
↓
5
**Recirculation (Zero New Water)**
The same cooled coolant returns to the cold plates. Closed loop means no evaporative water consumption — down from 2.6M gallons per MW/year to near zero.
The 45°C inlet temperature is what makes step 4 work without chillers — raise the loop temperature and the whole system gets dramatically more efficient.
Notice the dependency chain. The reason dry coolers can reject heat efficiently (step 4) is because the loop runs hot (steps 1–2). The reason water use drops to near zero (step 5) is because the loop is closed and chillers rarely fire (step 4). Pull any one of these out and the savings evaporate. This is a coordinated system, not a stack of independent upgrades — and that's precisely why most prior attempts at "liquid cooling" delivered a fraction of these gains. I've watched teams bolt cold plates onto racks while keeping their cooling towers running and wonder why the economics didn't move. This is why.
Industry estimates cited by NVIDIA suggest raising chiller plant temperatures by just 1 degree cuts cooling energy costs by ~4%. Going from a sub-room-temperature loop to 45°C compounds that effect across the entire facility — the single highest-leverage knob in data center efficiency. The ASHRAE thermal guidelines have been nudging this direction for years.
Coined Framework
The AI Coordination Gap (Applied)
In thermal systems, the gap is the difference between cooling each chip well and orchestrating chip-to-CDU-to-dry-cooler-to-recirculation as one loop. In agentic AI, it's the difference between a smart model and a smart system of models. Same failure mode, different domain.
Complete Capability List: Everything the Rubin Cooling System Delivers
100% liquid cooling — every chip and networking component, no fans anywhere in the system. (NVIDIA)
45°C (113°F) coolant inlet — full performance maintained, coolant exits at ~55°C.
Zero water consumption in the DSX reference design — closed loop, no evaporative cooling, chillers needed only ~1% of the year in some climates.
Up to 100% reduction in facility cooling water — from ~2.6 million gallons per MW/year to near zero.
$4M+ annual savings for a 50MW hyperscale facility in cooling-related energy and water.
Silent operation — eliminates fan noise that contributes to 85+ decibel levels requiring ear protection in traditional data centers. You can have a normal conversation in a Rubin hall.
No hot aisle / cold aisle choreography — ambient room temperature becomes flexible; warm summer air is fine.
Chiller-less operation in favorable climates using outdoor dry coolers.
DSX reference design — a full best-practices blueprint for designing, building, and operating the AI factory stack.
A 50-megawatt facility saving over $4 million a year on cooling isn't a rounding error — it's the difference between an AI factory that pencils out and one that doesn't.
How to Access and Use It: Availability and Implementation Path
This isn't a downloadable tool. It's an infrastructure reference design, and the path to adopting it is different depending on where you sit in the stack.
→
**Step 1 — Adopt the Rubin platform.**
Because the NVIDIA Rubin platform integrates 100% liquid-cooled infrastructure, every cloud provider and data center operator building for it makes the transition by default. There is no air-cooled Rubin variant — the cooling is mandatory, not optional.
→
**Step 2 — Follow the DSX AI factory reference design.**
NVIDIA's DSX reference design outlines best practices for the entire infrastructure stack — coolant chemistry, CDU placement, dry cooler sizing, and climate-based chiller fallback. Don't skip this and try to improvise. The interdependencies are real and they bite.
→
**Step 3 — Partner with the cooling ecosystem.**
Schneider Electric's Motivair division has worked alongside NVIDIA's roadmap for nearly a decade and supplies the advanced cooling hardware (cold plates, CDUs) validated for Rubin.
For software and AI teams who don't operate the metal but feel its effects: this is where your inference economics improve. Lower cooling overhead means lower cost-per-token from your cloud provider over time. If you're building agentic systems on top of this infrastructure, you can explore our AI agent library to design workloads that exploit cheaper, denser compute.
Implementing the DSX reference design means coordinating CDU placement, coolant chemistry and dry-cooler sizing — the orchestration layer that determines whether you capture the full $4M savings.
If you're building the software side that runs on this hardware, the same coordination discipline applies. Teams shipping multi-agent systems and enterprise AI pipelines should treat their orchestration layer with the same rigor NVIDIA applied to its thermal loop.
When to Use It (and When Not To)
The 45°C architecture is transformative. It's not universal.
Use it when: you're deploying high-density AI compute (Rubin-class GPUs), operating at hyperscale where the $4M/MW savings compound, or in a favorable climate where dry coolers can run chiller-less most of the year. Water-stressed regions get the biggest relative win from the near-100% water reduction — this can flip a location from legally untenable to viable overnight.
Be cautious when: you operate legacy air-cooled fleets that can't be retrofitted economically, or in extreme-heat climates where chillers may fire more than the cited ~1% of the year. The closed-loop economics still favor liquid over air, but the chiller-less benefit shrinks. Know your climate percentile data before committing to a dry-cooler-only design.
❌
Mistake: Keeping the data center cold "to be safe"
The decades-old instinct that a cold room means a healthy data center is exactly backwards. Over-cooling burns enormous energy for zero performance benefit — NVIDIA confirms chips sustain far warmer environments than intuition suggests.
✅
Fix: Decouple server thermals from ambient air entirely. With cold plates running at 45°C, room temperature becomes irrelevant and you stop paying to chill air nobody needs.
❌
Mistake: Treating liquid cooling as a component swap
Bolting cold plates onto chips while keeping chillers and evaporative towers captures only a fraction of the gains. This is the AI Coordination Gap in physical form — optimizing one part while the system stays misaligned.
✅
Fix: Adopt the full DSX reference design — closed loop, dry coolers, recirculation — so the hot loop and chiller-less rejection reinforce each other.
❌
Mistake: Ignoring water as a constraint
Conventional cooling-tower systems consume ~2.6M gallons per MW/year. In water-stressed regions, that's a regulatory and reputational liability that GPU benchmarks never mention. I've seen siting decisions blow up entirely over this.
✅
Fix: Model total cost of ownership including water. The closed-loop DSX design's near-zero water use is often the deciding factor for siting in arid regions.
Head-to-Head: 45°C Liquid Cooling vs Conventional Approaches
DimensionNVIDIA Rubin (45°C Liquid)Direct-to-Chip + ChillersTraditional Air Cooling
Coolant inlet tempUp to 45°C~20–30°C typicalN/A (air)
FansNoneSome (rack/facility)Many (85+ dB)
Cooling % of facility powerDramatically reducedReducedUp to 40%
Water use per MW/yearNear zeroOften high (towers)~2.6M gallons
Chiller dependency~1% of year (some climates)HighHigh in hot weather
Coolant chemistry75% water / 25% propylene glycolVariesN/A
50MW annual savings$4M+ vs airPartialBaseline
All Rubin figures sourced from NVIDIA's June 21, 2026 announcement. Competitor columns reflect general industry norms.
What It Means for Small Businesses
You don't run a 50MW facility. So why does this AI technology shift matter to you?
Because nearly every AI tool you use, from Anthropic's Claude to OpenAI's models, runs on this kind of infrastructure. When cooling costs drop and water liabilities vanish, the cost-per-token you pay trends downward over time, and providers can deploy denser compute in more regions — including regions closer to you.
Concrete opportunity: cheaper inference makes previously uneconomical AI features viable. A 30% drop in your provider's infrastructure overhead can be the difference between an AI feature that costs you $2,000/month to run and one that costs $1,400 — turning a money-loser into a margin-positive product line. Pair that with smart workflow automation and the math improves further.
Concrete risk: if you're committing to multi-year compute contracts, understand which infrastructure generation you're locked into. Air-cooled fleets will become a cost liability as Rubin-class liquid cooling becomes the standard. Don't sign a 3-year deal on infrastructure that's about to be obsolete.
The companies winning with AI infrastructure aren't the ones with the most GPUs — they're the ones who solved coordination across power, heat, and water. The same is true one layer up: the teams winning with agents solved orchestration, not model selection.
Who Are Its Prime Users
Hyperscalers & cloud providers building for Rubin — they capture the $4M/MW savings directly and pass partial savings downstream.
Data center operators in water-stressed or warm climates where the near-zero water use and chiller-less operation aren't nice-to-have — they're the whole siting decision.
Enterprise AI infrastructure teams planning private AI factories — the DSX reference design is their build blueprint.
Senior AI engineers and leads forecasting inference economics — cooling efficiency feeds directly into cost-per-token models.
Sustainability and ESG officers at large enterprises who must report on water and energy footprints.
How to Use It: A Worked Demonstration (Savings Model)
Let's make this concrete. Suppose you're an AI lead evaluating whether to site a new 50MW AI factory using the Rubin liquid-cooled design versus a conventional air-cooled build. Here's the back-of-envelope model, grounded entirely in NVIDIA's published figures.
Python — AI factory cooling savings model
NVIDIA Rubin 45C liquid cooling savings model
All figures sourced from NVIDIA's June 21, 2026 announcement
facility_mw = 50 # 50 MW hyperscale facility
--- Water savings ---
water_per_mw_conventional = 2_600_000 # gallons per MW per year (cooling towers)
water_conventional = facility_mw * water_per_mw_conventional
water_liquid = facility_mw * 0 # near zero with closed loop
water_saved = water_conventional - water_liquid
--- Cost savings (NVIDIA: 50MW facility saves $4M+/yr) ---
annual_savings_usd = 4_000_000 # energy + water, conservative floor
--- Cooling energy share lever ---
cooling_share_traditional = 0.40 # up to 40% of facility power on cooling
savings_per_degree = 0.04 # ~4% cooling cost cut per 1C higher
print(f'Water saved/year: {water_saved:,} gallons')
print(f'Cooling cost reduction: $4,000,000+ annually')
print(f'Over a 5-year deployment: ${annual_savings_usd * 5:,}')
Output
Water saved/year: 130,000,000 gallons
Cooling cost reduction: $4,000,000+ annually
Over a 5-year deployment: $20,000,000
Result: 130 million gallons of water saved per year and over $20 million across a five-year deployment — for a single 50MW facility. Scale that across a hyperscaler's fleet and the coordination win becomes existential, not incremental.
Good Practices and Common Pitfalls
Do adopt the full closed-loop DSX design — the savings come from the system, not isolated parts.
Do model your climate's chiller fallback hours before committing to a dry-cooler-only design; favorable climates approach chiller-less operation, but you need the data.
Do include water in your TCO — it's often the deciding siting factor.
Don't over-cool the room. Ambient temperature is now flexible — warm air is fine.
Don't assume air cooling is "good enough" — Motivair's Whitmore is direct: "Once the watts per chip crossed a certain level, liquid cooling became mandatory."
Don't lock into legacy infrastructure on long contracts as Rubin becomes the standard. This is the mistake I'd most want you to avoid.
Industry Impact: Who Wins, Who Loses
Winners: NVIDIA (Rubin's cooling is a differentiator competitors must now match), Schneider Electric / Motivair (a decade of co-development now pays off as the ecosystem standardizes around their hardware), operators in water-stressed regions, and ultimately every AI builder who benefits from cheaper, denser compute.
Losers: operators of legacy air-cooled fleets facing stranded-asset risk, and cooling vendors selling evaporative-tower-dependent designs. The economics have shifted permanently. That's not a prediction — it's already priced in. The International Energy Agency has flagged data center power demand as a structural concern, and the Uptime Institute tracks how quickly liquid cooling is displacing air, which only sharpens the pressure.
"Once the watts per chip crossed a certain level, liquid cooling became mandatory." When a Schneider Electric CEO calls your design choice mandatory, the debate is over.
Reactions: What Named Experts Are Saying
Josh Parker, author of the NVIDIA announcement, frames the 45°C choice as "one of the biggest efficiency leaps in data center history." (NVIDIA Blog)
Ali Heydari, director of data center cooling and infrastructure at NVIDIA: "We have eliminated massive amounts of power usage and pretty much all water usage."
Richard Whitmore, president & CEO of Motivair (Schneider Electric's advanced cooling division): "Once the watts per chip crossed a certain level, liquid cooling became mandatory." (Schneider Electric)
The Rubin AI factory: no fans, no cold aisles, near-zero water. The clearest physical proof yet that closing the AI Coordination Gap — not just adding components — is where the order-of-magnitude wins live.
[
▶
Watch on YouTube
NVIDIA Rubin liquid cooling and the AI factory explained
NVIDIA • Data center cooling architecture
](https://www.youtube.com/results?search_query=NVIDIA+liquid+cooling+AI+factory+Rubin)
What Happens Next: Roadmap and Predictions
2026 H2
**100% liquid cooling becomes the default spec.**
Because Rubin integrates 100% liquid cooling with no air-cooled variant, every operator building for it transitions by necessity — NVIDIA confirms "the ecosystem is keeping pace."
2027
**Competitors race to match warm-water designs.**
With cooling at up to 40% of facility power, AMD and cloud-native silicon vendors face real pressure to publish comparable chiller-less, near-zero-water designs. This isn't optional anymore.
2027–2028
**Water becomes a primary siting constraint.**
The near-zero water profile (vs 2.6M gal/MW/year) makes liquid-cooled AI factories viable in arid regions previously off-limits — reshaping where compute gets built globally.
Frequently Asked Questions
How does NVIDIA's 45°C AI technology cooling actually save energy and water?
The 45°C liquid-cooling AI technology saves energy and water because a hotter loop can reject heat through outdoor dry coolers instead of energy-hungry mechanical chillers for most of the year. Coolant (75% water, 25% propylene glycol) enters the chip's cold plate at 45°C and exits near 55°C in a closed loop that recirculates the same liquid indefinitely — so there is no evaporative water loss. Because cooling has historically consumed up to 40% of facility power and conventional towers burn ~2.6M gallons of water per MW/year, eliminating chillers and evaporation drives both numbers toward zero. NVIDIA reports $4M+ annual savings for a 50MW facility. The deeper reason it works is coordination: chip thermals, coolant chemistry, CDU routing, and dry-cooler rejection are tuned as one coupled system rather than optimized in isolation.
What is agentic AI?
Agentic AI describes systems where models don't just answer prompts but autonomously plan, take actions, call tools, and pursue multi-step goals. Frameworks like LangGraph, AutoGen, and CrewAI orchestrate these agents. The connection to this article is direct: just as NVIDIA's cooling win came from coordinating chip, CDU and dry cooler as one system, agentic AI's value comes from coordinating multiple specialized agents — not from any single model. The AI Coordination Gap is exactly where most agentic deployments fail: teams build capable individual agents but neglect the orchestration layer that makes them reliable together. Production-ready agentic stacks treat coordination as the primary engineering problem, not an afterthought.
How does multi-agent orchestration work?
Multi-agent orchestration coordinates several specialized AI agents — a planner, a researcher, a coder, a critic — toward a shared goal, managing state, message passing, and handoffs between them. LangGraph models this as a stateful graph; AutoGen uses conversational agents. The critical insight: a six-step pipeline where each step is 97% reliable is only ~83% reliable end-to-end. Orchestration exists to manage that compounding failure through retries, validation, and routing. This mirrors NVIDIA's thermal loop — every handoff (chip→CDU→dry cooler) must coordinate or the whole system underperforms. Strong orchestration layers add observability, fallback paths, and explicit state management, closing the AI Coordination Gap that otherwise quietly degrades production reliability.
What companies are using AI agents?
Major adopters span finance, software, and customer support. OpenAI and Anthropic ship agentic capabilities directly in their APIs. Enterprises use workflow automation platforms like n8n to wire agents into business processes. The infrastructure these agents run on is exactly what NVIDIA's Rubin platform powers — and cheaper, denser liquid-cooled compute lowers the cost of running agent fleets at scale. The companies seeing real ROI aren't the ones with the most agents; they're the ones who solved coordination, observability, and failure handling. That's the recurring theme: components are cheap, coordination is the moat. You can browse ready-made agents to see coordination patterns in practice.
What is the difference between RAG and fine-tuning?
RAG (Retrieval-Augmented Generation) injects relevant external knowledge into a model's context at query time using a vector database like Pinecone — ideal for frequently changing facts and source attribution. Fine-tuning adjusts the model's weights to bake in style, format, or domain behavior — better for consistent tone and specialized tasks. RAG is cheaper to update (just re-index documents); fine-tuning requires retraining. Most production systems use both: fine-tune for behavior, RAG for knowledge. The choice is a coordination decision — which layer owns which responsibility. Getting that boundary wrong is another flavor of the AI Coordination Gap, where teams over-invest in one technique while neglecting how it integrates with the rest of the stack.
How do I get started with LangGraph?
Start with the official LangGraph documentation. Install via pip install langgraph, then model your workflow as a graph: define nodes (agents or functions), edges (transitions), and shared state. Begin with a simple two-node loop — an agent and a validator — before scaling to complex topologies. Add checkpointing for persistence and observability from day one; debugging multi-agent systems without state visibility is brutal. For deployment patterns and pre-built agents, explore our AI agent library. The most common beginner mistake is building too many agents too fast — start with the minimum viable coordination and add complexity only when a simpler design provably fails. LangGraph is production-ready and widely used in enterprise deployments.
What is MCP in AI?
MCP (Model Context Protocol) is an open standard from Anthropic that standardizes how AI models connect to external tools, data sources, and systems. Instead of writing custom integrations for every tool, MCP provides a universal protocol — think of it as a standardized port for AI context. This is itself a coordination solution: it closes the AI Coordination Gap at the tool-integration layer by giving agents one consistent way to discover and call capabilities. For builders, MCP dramatically reduces the engineering tax of wiring agents into enterprise systems. It's rapidly being adopted across the ecosystem and pairs naturally with orchestration frameworks like LangGraph and AutoGen. MCP is production-ready and increasingly the default integration standard for agentic AI.
The deeper lesson of NVIDIA's hot-tub coolant isn't about temperature. It's that the largest gains in AI technology — whether in silicon or in software — come from closing the AI Coordination Gap: orchestrating the system, not perfecting the parts. The teams who internalize that, at every layer of the stack, are the ones who win.
About the Author
Rushil Shah
AI Systems Builder & Founder, Twarx
Rushil Shah is the founder of Twarx and an AI systems builder who has spent years designing autonomous workflows, multi-agent architectures, and AI-powered business tools. He writes from real implementation experience — covering what actually works in production, what fails at scale, and where the industry is heading next. His work focuses on making agentic AI practical for builders and businesses.
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