You can’t build complex AI systems on shifting sand. We are fanatically focused on AI inference that is deterministic, lossless, high-speed, and audit ready.
Joined January 2026
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May 13
We built the "Impossible" Qwen 14B Demo:
1) Lossless Compression, Full Precision
2) Cross-GPU Determinism
3) Zero Knowledge Proof
4) Improved Speed
5) Hardware Privacy
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May 4
Congrats to the DeFacts team who used Paradatum deterministic AI to move to Round 2 with their ETHGlobal Hackathon Submission!
Friends don't let friends use non-deterministic AI!
ETHGlobal | Open Agents
ethglobal.com/events/openage…
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Mar 17
A complete validation of the Paradatum thesis.
Determinism is an absolute requirement for the future.
Mar 17
AI-native services are exposing a new bottleneck in AI infrastructure.
The challenge is shifting from peak training throughput to delivering deterministic inference at scale with predictable latency, jitter, and sustainable token economics.
Check out our latest tech blog to see how AI grids make real-time, multi-modal, and hyper-personalized AI experiences viable at scale.
📡 Read now: nvda.ws/3NK1Oiu
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Mar 9
Determinsitic AI is the only solid foundation to build upon.
Some of the stories they aren't telling you:
• Chevrolet's chatbot sold a car for $1
• Air Canada had to honor a refund policy that its chatbot made up
• A pipeline ran 20x over cost for 6 days without anyone noticing
People didn't realize because nothing broke. There were no crashes and no alerts.
That's the issue with agentic applications.
They always generate something that looks coherent and don't raise any suspicion unless it's too late.
There's an amazing free YouTube lecture and blog post from @arshdilbagi that will help you fix this with a practical framework.
Here is what you'll learn:
• How to set up end-to-end trace instrumentation
• How to build alerts around a silent failure taxonomy
• An eval system built from production data
• Complete and concrete implementation steps
Every section of the blog ends with exactly what to do next.
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Mar 9
Yes. Future performance will not be solved by throwing more tensor cores at the problem. Better algorithms are required.
Mar 8
"FlashAttention-4"
This new iteration of Flash Attention shows that on NVIDIA Blackwell GPUs the new bottleneck in Transformer attention isn’t matmul anymore but softmax shared-memory traffic.
So the latest designs stop treating attention as just GEMMs and start co-designing the whole kernel around the slow parts.
On B200, it is able to hit ~1600 TFLOPs/s and beats cuDNN/Triton, as this now addresses the real bottlenecks Blackwell exposes.
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Feb 23
The goal is sound. But making AI more prone to lossy garbage is not the way.
Feb 23
Scaling LLMs shouldn’t mean compromising on speed or budget.
Our latest research demonstrates how low-precision training using NVFP4 and MXFP8 on NVIDIA Blackwell GPUs boosts throughput by up to 1.6x while maintaining BF16-level accuracy.
Using NVIDIA NeMo libraries, researchers can now deploy these recipes for massive memory savings and faster convergence.
Read our full technical deep dive 👇
developer.nvidia.com/blog/us…
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Feb 23
Hype train don’t care about ROI
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Feb 4
This is artificial intelligence deciphering a 2,000 year old Greek manuscript.
The future is here:
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Feb 4
Anthropic still misses the point.
Even if a larger model is "less bad" on reproducibility, when agents call other agents the drift still compounds.
The answer is simply to use a deterministic version of the same model. Bit-exact answers... every time.
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Feb 1
Up close with Booster 19 rolling out to Massey's test site tonight for initial pressure and cryo proof testing in preparation for Starship test flight 12.
2/1/26
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Feb 1
This is cool! Our work involves running sub graphs of models. How amazing it would be to be able to visualize the constraints of all possible "World History" reasoning pathways for instance.
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Feb 1
NVIDIA is racing to supply more memory. The memory supercycle rewards whoever can do the most with the fewest bytes.
Paradatum reduces how much memory the system fundamentally needs by making inference compressed, deterministic, reusable, and provable.
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Jan 25
Intelligence assumes a deterministic world.
Once you understand that intelligence is deterministic and not probabilistic, you'll begin to grasp how foolish it was to think that deep learning was the correct tool for solving the intelligence problem.
Determinism has to do with the precise timing of discrete events in the real world. This is the reason that biological intelligence uses spiking neurons. Intelligence is a neuroscience problem.
The AI industry's obsession with probability prevents them from seeing the light. This is a good thing, imo, because the industry is infested with liars, scammers and other malevolent souls. They deserve to remain in their ignorance. 😀
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Jan 31
Not really.
This isn’t “Blackwell is faster.”
It’s “NVIDIA is redefining efficiency.”
NVFP4 isn’t portable compression — it’s a format only Blackwell can execute. Everyone else has to inflate it back to 16-bit to run.
That’s not optimization, it’s bottleneck relocation.
The 99.4% accuracy headline misses the point. The real shift is this: efficiency now means which chip gets to interpret the bits
That’s smart vendor lock-in. Very NVIDIA.
But it’s a step backward for an open, portable AI stack.
As a side note, 99.4% accuracy is like 99.4% bulletproof glass.
Maybe it’s useful… but I sure wouldn’t trust my life to it.
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