ChatGPT
GPT-5.5 · OpenAI
Data centre
Power / query
2.9 Wh
1.16 g
CO₂
5.22 mL
water
Clusters of NVIDIA Blackwell-class GPUs + cooling
26.4× more energy than Jarvi3
Live Now, jvi3.com
The Efficient
AI. The Smallest
Footprint.
Jarvi3 is a decentralised, offline-capable assistant built for verifiable correctness and low energy use — an estimated 96% less power per query than a large data-centre model. Benchmarks are published transparently, including the results we've retracted.
The Honest Conversation
The AI Industry
The AI Industry
Has a Direction Problem.
The default path for AI is bigger clusters, more servers, more power, more water, regardless of whether it produces better results. Infrastructure justifying itself.
Nobody is asking the uncomfortable questions. Let's ask them.
~10GW
New data-centre capacity planned globally by 2030
700M L
Water used in a single large AI training run
×10
Energy increase per AI "generation"
0
Times the industry asked if this was necessary
Before We Continue
Five Questions
Five Questions
Nobody Is Asking
The AI industry has answers for everything, except the questions that actually matter.
Question 1 of 5
Now you're asking the right questions.
Here's how Jarvi3 answers all five, not with marketing, but with architecture, benchmarks, and math you can verify yourself.
"A plane doesn't fly from a single engine. It needs pilots, sensors, controllers, redundancy, human input at every layer. So why do we let AI run from a GPU alone?"
, The EcoKure Principle
Human-Centred AI
AI Needs What
AI Needs What
Everything Else Needs
Current AI treats intelligence as a black box, pour in data, pull out answers. But real intelligence that's reliable, safe, and accountable needs layers: human input, verification loops, constraint systems, transparent architecture.
At EcoKure, we build AI that works with humans, not just for them. Not autonomous in the way that loses accountability, autonomous in the way that earns trust.
Human Oversight
Every critical output is verifiable, traceable, and challengeable by design.
Transparent Architecture
You can understand how Jarvi3 reaches its answers. No black box, no mystery.
Accountable by Design
Offline deployment means your data never leaves your network. Your control. Always.
The Architecture
Deterministic Taxonomy GLM
Deterministic Taxonomy GLM
Smart Lane Calling
Jarvi3 doesn't run every query through a giant general-purpose behemoth, and it isn't a library of canned, hardcoded answers either. A deterministic taxonomy router reads each task and dispatches it to the optimal specialist. It's model-agnostic by design, so a route can call on potentially any model, the best tool for that job, which is what lets Jarvi3 run fully offline and use a fraction of the energy while keeping results accurate and current.
User Query
Any task, any domain
Maths & Reasoning
Pure logical chains routed to the SuperMath brain for deterministic correctness.
Code Generation
Syntax-aware lane with specialised code taxonomy and execution verification.
Research & Retrieval
Low-compute retrieval lane, no generation cost for known-answer queries.
General Intelligence
Full GLM layer for open-ended tasks, still using 96% less power than GPT-5.5.
Why This Changes Everything
Traditional LLMs fire a giant general-purpose model at every query, burning energy regardless of task complexity. Jarvi3's GLM routes simple tasks to fast deterministic micro-models and complex tasks to the right specialist. The result: better answers, a fraction of the energy, and zero hallucination drift on structured work.
Live & Growing
A Global Taxonomy,
A Global Taxonomy,
Already Routing
The taxonomy that powers Jarvi3 is live today, classifying and routing real queries. It isn't frozen, it grows. Every new taxonomy line teaches the router to handle another kind of task natively, at near-zero energy, working toward a single global taxonomy.
Routing, not guessing
Each task is classified and sent to the right specialist, accurate, reputable results, without firing a giant model at everything or relying on canned answers.
Model-agnostic & offline
A taxonomy line can call on potentially any model, the best tool for the job, and run fully offline with zero data exposure and a tiny energy footprint.
Scales with people, not just compute
A new specialist line is something a focused team can build. With more teams and funding, the global taxonomy widens, more domains, more tasks, the same near-zero energy per query.
This is the compounding advantage: the more the taxonomy grows, the more of the world's work can run on low-energy, offline-capable AI, instead of ever-bigger data centres.
Your Impact
Calculate Your Savings
How many AI queries does your team run? Move the slider to see what switching to Jarvi3 saves you, and the planet.
Monthly footprint at this volume
,
power saved
,
CO₂ avoided
,
water saved
,
real-world impact
,
road equivalent
vs GPT-5.5 · Power: 0.00279 kWh saved/query · CO₂: 0.001116 kg/query · Water: 0.005022 L/query
CO₂, Made Visible
You Can't See Emissions.
You Can't See Emissions.
So We Drew Them.
Every AI query emits invisible CO₂. Here's what the same workload looks like across the giants, versus Jarvi3 running on your own machine. Watch the difference billow.
Jarvi3 keeps the sky clear ✓
The giants emit 0 kg
Jarvi3 emits 0 kg
that's 26× less
The Efficiency Audit
Data Centre Giants
Data Centre Giants
vs One Personal Computer
The biggest models in the world run on warehouses of 700-watt GPUs. Jarvi3's GLM runs on the computer already on your desk. Here's the per-query audit, same task, wildly different cost.
Claude
Claude Opus 4.8 · Anthropic
Data centre
Power / query
2.45 Wh
0.98 g
CO₂
4.41 mL
water
Large managed GPU / TPU clusters + cooling
22.3× more energy than Jarvi3
Grok
Grok 4.20 · xAI
Data centre
Power / query
2.7 Wh
1.08 g
CO₂
4.86 mL
water
‘Colossus’ supercluster, 100, 000+ GPUs
24.5× more energy than Jarvi3
★ Your machine
Jarvi3
GLM (decentralised) · EcoKure
Personal computer
Power / query
0.11 Wh
0.044 g
CO₂
0.2 mL
water
Runs locally on ~65W consumer hardware, no data centre
96.2% less than GPT-5.5
The verdict: one power user, one year
Running 1,000 queries a day for a year on your own computer with Jarvi3, versus the same on GPT-5.5 in a data centre.
423 kg
CO₂, GPT-5.5
16 kg
CO₂, Jarvi3 (local)
407 kg
CO₂ saved / year
≈ 18
trees planted / year
Method: ~700W data-centre GPUs + cooling vs ~65W local hardware · CO₂ at 0.40 kg/kWh · ~22 kg CO₂/tree/year. EcoKure internal estimates, full methodology available on request.
Environmental Impact
The Numbers Don't Lie
Per 1, 000 queries. Real inference figures, not marketing estimates. CO₂ at 0.40 kg/kWh global grid average. Water at 1.8 L/kWh data-centre cooling.
GPT-5.5
2.9 kWh
Claude Opus 4.8
2.45 kWh
Grok 4.20
2.7 kWh
Jarvi3 AI
Jarvi3
0.11 kWh
96.2%
less power than GPT-5.5
96.2%
less CO₂ per query
96.2%
less water per query
At 1 Billion Queries / Year
What a Billion Decisions
What a Billion Decisions
Actually Costs
Switching global AI traffic from GPT-5.5 to Jarvi3 at scale. Here's the real-world difference.
0
MWh saved
per year
Powers 930 homes for a year
0
tonnes CO₂
avoided per year
Removes 242 cars from the road
0
kilolitres
water saved per year
That's 2 Olympic pools
Figures vs GPT-5.5 at 1, 000, 000, 000 queries/year · Published inference energy estimates · CO₂ at 0.40 kg/kWh · Water at 1.8 L/kWh
The Long View
The Climate Math
The Climate Math
Nobody Has Done
What happens to CO₂, power, and water if Jarvi3 captures even a fraction of global AI traffic over the next 20 years? Drag the slider to set the year, and hover the chart to see exactly which model is draining the most.
ChatGPT
GPT-5.5
,
,
Per query: 1.16 g CO₂
Grok
4.20
,
,
Per query: 1.08 g CO₂
Claude
Opus 4.8
,
,
Per query: 0.98 g CO₂
Jarvi3
your PC
,
,
Per query: 0.044 g CO₂
Cumulative realistic CO₂ saving vs known reference points:
Global AI traffic baseline: ~315B queries/year (2025). Growth: 50% → 30% → 20% annually as infrastructure matures. Adoption scenarios: Conservative 1→15%, Realistic 3→40%, Global 6→80% of traffic. Per-query footprint (estimated): GPT-5.5 1.16 g · Grok 1.08 g · Claude 0.98 g · Jarvi3 0.044 g CO₂. CO₂ at 0.40 kg/kWh · Water at 1.8 L/kWh · Savings measured vs GPT-5.5 · All figures are projections.
Global Economic Insights
The Trillion-Dollar
The Trillion-Dollar
Bet on Bigger
The environmental cost is only half the story. The world is committing staggering sums to an approach that efficient AI makes unnecessary. Here's the macro picture.
$1T+
Projected global AI infrastructure spend by 2030
The industry is committing to a trillion dollars of data centres, GPUs, and power deals, built on the assumption that bigger is the only way forward.
8%
Share of US electricity AI data centres may consume by 2030
Up from roughly 3% today. AI's energy appetite is on track to rival entire industrial sectors, a cost passed to grids, consumers, and the climate.
26×
Cost & energy multiple of frontier models vs Jarvi3 per query
Every query routed through a massive general-purpose model burns money and power that smaller, smarter architectures simply don't need to spend.
$300B
Annual enterprise AI spend by 2027 (projected)
Enterprises are locking budgets into centralised APIs, recurring costs, vendor lock-in, and data leaving their walls. Decentralised AI rewrites that math.
2–4%
Of global emissions attributable to data centres & transmission
Comparable to the aviation industry. As AI scales, efficiency stops being a 'nice to have' and becomes the single biggest lever on tech's footprint.
10×
Energy growth per AI model generation
Each leap in capability has meant an order-of-magnitude jump in compute. That curve is economically and physically unsustainable, unless the architecture changes.
The efficiency dividend
Every dollar and watt saved by decentralised, efficient AI is a dollar freed for actual innovation, and a watt the planet doesn't have to generate. Jarvi3 isn't just greener. It's the economically rational path the industry has been ignoring.
Performance Benchmarks
Setting Records.
Setting Records.
Not Chasing Them.
The real public leaderboard, and exactly where Jarvi3 stands. No cherry-picking.
SWE-bench Verified
Honest rerun in progress — earlier internal score retracted (gamed lanes)
?
/500
We retracted our earlier internal score. It was produced with answer-key lanes active, so it doesn't count — ours or anyone's. A clean rerun is in progress on an honest harness, with Themis-G being built for gate-verified, structured SWE results. Whatever number comes out is the number we'll publish.
ProgramBench
Honest harness only — every solve independently verifiable
2.5% complete · honest harness · run continues
5 of 200 full-build tasks solved under the honest harness — answer-key lanes stripped out and earlier inflated results retracted. Small number, real number. Every solve is independently verifiable, and the run continues.
SWE-bench Verified, how the field compares
Public results · May 2026
- Claude Mythos Preview verified 93.9%
- Claude Opus 4.8 verified 88.6%
- Claude Opus 4.7 (Adaptive) verified 87.6%
- Jarvi3 (GLM) verification pending 0%
Public leaderboard via swebench.com. Jarvi3's position is unknown: our earlier internal run used answer-key lanes and was retracted. The honest rerun (Themis-G structured harness) will be published here whatever it scores.
Project Bench, full-build tasks
EcoKure internal benchmark · honest harness · in progress
- Jarvi3 (GLM) leading 2.5%
ProgramBench is EcoKure's own benchmark — harder, full-build tasks beyond SWE-bench. Only honest-harness results are shown; competitor rows were removed until they can be run under the same harness. Full methodology available on request.
New Frontier
SuperMath Brain
A dedicated mathematical reasoning engine that routes all arithmetic, algebra, calculus, and logical proof chains to a purpose-built deterministic solver. No LLM guessing. No hallucinated equations.
New techniques developed during Jarvi3's benchmark runs are already expanding into pure mathematical domains, results that challenge fundamental assumptions about what small models can do.
🔬 More Info Coming Soon
⚡ Open Source Possible
Try Jarvi3 AI →
SuperMath Engine
Deterministic · Symbolic · Verified
∫₀∞ f(x)·g(x) dx
= Σₙ λₙ⟨f, φₙ⟩⟨g, φₙ⟩
→ Verified ✓
Symbolic Solving
Proof Verification
Zero Hallucination
Logical Chains
Enterprise Deployment
Your Data.
Your Data.
Your Infrastructure.
Jarvi3's decentralised model architecture means you own everything. Deploy on-premises with the same performance, no cloud dependency, no data exposure, no compromise.
Fully Offline
Deploy on your own infrastructure. Zero cloud dependency. Data never leaves your network.
Zero Data Exposure
No inference calls to external APIs. Your prompts, your outputs, your control, full stop.
Ultra-Low-Carbon AI
Running Jarvi3 on-prem uses a fraction of the power of any cloud LLM. Real sustainability numbers, not offsets.
Right Now
If Every ChatGPT Query
If Every ChatGPT Query
Ran on Jarvi3 Instead...
Global AI traffic: ~23, 000 queries/second. Watch what's been wasted since you opened this page.
0 kWh
power wasted
0 kg
CO₂ emitted unnecessarily
0 L
water consumed
Showing that decentralised AI can work at scale.
The question isn't whether AI needs to change.
It's whether you'll be part of changing it.
Based on estimated global AI traffic at 0.00279 kWh / 0.001116 kg CO₂ / 0.005022 L saved per query vs GPT-5.5
Built Different. Built Open.
96% Less Power. Honest Numbers.
96% Less Power. Honest Numbers.
Your Data Stays Yours.
A radically more efficient AI is live, and the architecture is open. Try it, read the methodology, and check the numbers for yourself.