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Been looking into token optimization and model routing, I think super obvious optimization to tackle both cost + demand on inference Here’s a small post about different techniques and methods

Sean Geng

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16,405 görüntüleme • 25 gün önce •via X (Twitter)

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$NBIS cofounder Roman Chernin describes how their recent acquisitions of Eigen AI and Clarifai were all about speed, incredible talent, and acceleration: "The philosophy is very simple. We need to build so many things, and we need to move so fast, that we're always looking for people who can accelerate us. It should be exceptional talent, and/or something that has a great adoption." "Our two recent acquisitions [were] two teams that work on inference optimization. A big part of our business is how efficiently we convert GPUs into tokens. And these two teams — Eigen AI and Clarifai — one is focused on model optimization, the engine of inference. How you run specific models and all the techniques around spec decoding, quantization, and so on." "And the other is system optimization. All the routing, KV caching, and orchestration across the big cluster of compute and so on." "We have a very strong internal team working on inference. But we felt that we needed to move faster, bring more capabilities. Because the market is so fast."

$NBIS cofounder Roman Chernin describes how their recent acquisitions of Eigen AI and Clarifai were all about speed, incredible talent, and acceleration: "The philosophy is very simple. We need to build so many things, and we need to move so fast, that we're always looking for people who can accelerate us. It should be exceptional talent, and/or something that has a great adoption." "Our two recent acquisitions [were] two teams that work on inference optimization. A big part of our business is how efficiently we convert GPUs into tokens. And these two teams — Eigen AI and Clarifai — one is focused on model optimization, the engine of inference. How you run specific models and all the techniques around spec decoding, quantization, and so on." "And the other is system optimization. All the routing, KV caching, and orchestration across the big cluster of compute and so on." "We have a very strong internal team working on inference. But we felt that we needed to move faster, bring more capabilities. Because the market is so fast."

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The defining differentiator in AI right now isn't model performance, it's cost per token. Because in this market, efficiency is what turns demand into actual growth. Roman Chernin of Nebius outlines why inference economics are becoming the central challenge, as companies operating on thin margins need infrastructure that can scale without eroding profitability. Token Factory reflects that shift, focusing on orchestration, optimization, and full-stack control to make AI systems economically viable.

The defining differentiator in AI right now isn't model performance, it's cost per token. Because in this market, efficiency is what turns demand into actual growth. Roman Chernin of Nebius outlines why inference economics are becoming the central challenge, as companies operating on thin margins need infrastructure that can scale without eroding profitability. Token Factory reflects that shift, focusing on orchestration, optimization, and full-stack control to make AI systems economically viable.

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[ Model Optimization ] Here's a comparison video of the Female Dancer's second optimization (the third one). Her legs are no longer thick as they were in the first optimization, and her chest size has been reduced to match the original model. ※ Source:

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As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

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the 3 phases of AI sticker shock: 1. board asks “whats our AI strategy?” 2. company says “everyone use AI for everything” 3. the token bill arrives, the crackdown begins ...and model routing starts to look pretty obvious. Matan Grinberg: "“There are different models that are good at different tasks. They have different trade-offs between cost, quality, and speed"

the 3 phases of AI sticker shock: 1. board asks “whats our AI strategy?” 2. company says “everyone use AI for everything” 3. the token bill arrives, the crackdown begins ...and model routing starts to look pretty obvious. Matan Grinberg: "“There are different models that are good at different tasks. They have different trade-offs between cost, quality, and speed"

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Learn to optimize RAG for cost and performance in our new short course, Prompt Compression and Query Optimization, created with MongoDB and taught by Richmond Alake. This course teaches you to combine traditional database capabilities with vector search using MongoDB for RAG. You'll learn these techniques: - Vector search: For semantic matching of user queries - Filtering using metadata: Pre- and post-filtering to narrow search results - Projections: Selecting only necessary fields to minimize data returned - Boosting: Reranking results to improve relevance - Prompt compression: Using a small LLM to compress context, significantly reducing token count and processing costs These methods address scaling, performance, and security challenges in large-scale RAG applications. You can sign up here:

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bro casually walks and explains 5 GPU performance optimization methods for LLMs. one of the most simple and intuitive explanations for beginners.

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$NBIS co-founder Roman Chernin explained why the next AI bottleneck is running inference efficiently at scale. That’s why Nebius is building Token Factory around model optimization, orchestration & agentic AI deployment instead of acting like a basic GPU rental business.

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Jim Cramer on Leopold Aschenbrenner’s $NBIS stake “When this man touches something, it turns to gold.” The next AI bottleneck is efficient inference at scale which is why Nebius Token Factory is built around optimization, orchestration and agentic deployment rather than basic GPU rental.

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[ Model Optimization ] Here's a comparison video of the Mercenary's optimization. He now has hair underneath his hood, he's no longer a bald bean. ※ Source:

[ Model Optimization ] Here's a comparison video of the Mercenary's optimization. He now has hair underneath his hood, he's no longer a bald bean. ※ Source:

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