This futuristic molecular structure diagram metaphors Intern-S1-Pro’s ambition: it doesn’t want to generate your social media captions; it wants to reconstruct the underlying logic of the physical world.
Just Graphics Cards Screaming? No, It’s Science Trembling (Deep Insight)
If ChatGPT is the socialite among liberal arts students, then Intern-S1-Pro is the stern STEM researcher sitting in the corner of the lab, wearing a white coat with a piercing gaze.
In the year 2026, a trillion parameters is hardly news anymore. Every major tech giant’s PowerPoint has long since stacked parameter counts to the moon. Yet, the emergence of Intern-S1-Pro has still caused the long-silent circle of “Scientific Computing” to tremble three times over.
Why? Because its “bigness” isn’t designed to chat with you about horoscopes.
This is a native, scholarly AI4Science model. While most models grew up bathing in the massive ocean of internet detritus, Intern-S1-Pro’s formula tin was filled with chemical equations, protein structures, and geological exploration data.
Its most compelling feature is its “rigorousness that refuses hallucinations.” Asking it “how to make tomato and scrambled eggs” is a waste of talent; but ask it “how the lattice structure of this new alloy changes under these temperature and pressure conditions,” and its 512 expert brains begin to spin at high speed. This is not just the violent aesthetics of computing power, but a “brute force cracking” of the physical world’s underlying logic. It proves that the next high ground for AI lies not in who can write the most moving love letter, but in who can calculate the next high-temperature superconducting material.
The Abacus of 512 “Zhuge Liangs” (Independent Perspective)
Everyone is competing on MoE (Mixture of Experts) architectures, but Intern-S1-Pro is competing in a somewhat “perverse” way.
Typically, MoE models have a double-digit number of experts, capping out at around 64. Intern-S1-Pro, however, went straight for 512 experts.
What does this mean? It means extreme professional specialization.
Imagine that instead of an omniscient god living in your head, there are 512 specialized doctors. When processing text, only 8 relevant experts wake up; when handling chemical bonds, a different set of 8 experts takes over. Each inference only activates 22B (22 billion) parameters.
This isn’t just to save electricity; it’s for “clarity of thought.”
Even more impressive is its Thinking Mode. This thing is essentially a “scientific replica” of OpenAI o1. The default thinking mode allows the model to engage in an implicit chain-of-thought deduction before spitting out an answer.
For writing poetry, this pause is a stutter; but for scientific reasoning, this pause is a “necessary silence.” It is weighing, verifying, and simulating. It’s like the deep breath an old professor takes before writing a formula on the blackboard—that’s not dazing off, that’s building the path to truth.
Visualization of Fourier Position Encoding (FoPE). Don’t get dizzy looking at these wavy lines; this is essentially the “black tech” AI uses to “remember” every single time point when processing time-series data stretching into the millions.
Refusing to be a “Chat Buddy,” It Wants to be the “Lab Director” (Industry Context)
You won’t know how hardcore it is until you compare it with the competition.
Horizontally comparing current open-source models, most are still general-purpose LLMs (Large Language Models). If you ask them to analyze a segment of seismic wave data containing $10^6$ points, they will likely burn from the CPU down to the motherboard, or simply spout gibberish.
Intern-S1-Pro unleashes FoPE (Fourier Position Encoding), combined with upgraded time-series modeling, to brute-force swallow heterogeneous time series of million-level lengths. In the field of physical signal processing, this is simply a dimensionality reduction attack.
Then look at deployment costs. Ordinary people wanting to run this at home? Go to bed early.
The official documentation is blunt: Running a trillion-parameter model using the native Hugging Face forward method is challenging. (Running a trillion-parameter model with native HF is asking for trouble).
It relies heavily on high-performance inference engines like LMDeploy or vLLM. This destines its home to be not your personal computer, but the server clusters of universities, research institutes, and enterprises. It is not here to democratize AI; it is here to arm scientists.
Moreover, it natively supports Tool Calling. This means it’s not just a “test-taker”; it can pick up a “wrench”—calling external APIs to get real-time temperatures or running Python code to simulate experiments. It is evolving from a “brain” to an experimenter that uses “hands and brain together.”
Can These One Trillion Synapses Calculate the Next Newton? (Unfinished Thoughts)
Although I am intimidated by the array of these 512 experts, I still have to pour a bucket of cold water (or rather, offer a cup of hot tea).
Intern-S1-Pro has slaughtered the Benchmarks, but does this really mean the automation of scientific discovery?
Our current AI4Science is often still “fitting” known scientific laws, rather than “discovering” unknown laws.
These 512 experts are, after all, fed on scientific data that humans already possess. They can be extremely efficient at deducing from A to B within the known knowledge graph, or even finding shortcuts from A to Z that humans hadn’t noticed.
But, can they jump out of this graph and propose a brand-new paradigm?
For example, before the theory of relativity was proposed, no amount of data could train an Einstein.
If Intern-S1-Pro can only be an extremely diligent experimenter, then it remains a tool. What I look forward to more is the day its Thinking Mode suddenly freezes and outputs the sentence: “Humans, your previous formulas seem to be derived incorrectly.” That moment will be AI’s highlight.
The Final Donut (Reflections)
Writing this, I’m reminded of the donut shop downstairs from the lab.
Technological progress sometimes looks a lot like making donuts: the dough gets kneaded tougher, the frosting gets thicker, and the parameters stack higher. Intern-S1-Pro is undoubtedly the most solid, substantial donut.
It not only demonstrates the resilience of the open-source community (Apache-2.0 license, a huge thumbs up for this) in catching up to or even surpassing closed-source models, but also shows us a possibility: Science is no longer the flash of genius from a lonely individual, but can become the inevitable output of stacked computing power.
Regardless, when machines begin to look up at the stars and calculate orbits for us, we had better ensure that we are still holding the joystick that determines the direction.
After all, no matter how fast it calculates, defining “what is a good question” remains up to us.
References：
- InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model
- Smarter, faster AI models explored for molecular, materials discovery
- Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding
- Intern-S1: A Scientific Multimodal Foundation Model
—— Lyra Celest @ Turbulence τ
