Founding Story: From Finance to AI Infrastructure
A Non-Linear Path to AI Infrastructure
Tuhin’s career began in investment banking, but boredom led him back to engineering and machine learning research. His early startup experiences eventually converged on building Base 10 to support the explosive growth of AI.
Career Journey
Let's build an infrastructure business alongside it ... production inference.
Customer Spotlight: Powering Voice AI and Healthcare Scribes
Full-Stack AI Infrastructure
Base 10 runs all optimizations and infrastructure for its customers, managing multiple models to ensure low latency and high reliability, so companies can focus on their core product.
Models Managed per Customer
We run all the optimizations, we run all the infrastructure so that latency from the time you talk to when text shows up is as quick as possible.
Why Base 10 Over Hyperscalers: Performance, Reliability, and Developer Experience
The Base 10 Thesis
While 95% of inference spend goes to frontier models, the path to profitable, defensible AI companies lies in custom models—and Base 10 aims to be the platform where they run.
The Base 10 Migration Path
- Companies first try hyperscalers (AWS, GCP, Azure) for inference.
- They struggle with DIY optimizations, reliability, and tooling.
- They move to Base 10 for an integrated, fault-tolerant platform.
they realize they'll just be better served coming to base 10.
The Economics of Post-Trained Open Source Models
The dual rationale for post-training open source models
Viable: Companies scaling from product-market fit need to boost gross margins from near 0% to 40–70% by running open source models 70–90% cheaper. Cynical: Using frontier models risks giving away proprietary data and user signals, allowing frontier labs to eventually compete with your core workflows.
How to own your intelligence
- Adopt open source models that are ~90 days behind but 70–90% cheaper
- Post-train on proprietary data to create specialized models for your workflows
- Drive gross margins from near zero to 40–70% while maintaining quality
- Stay independent from frontier labs to protect your unique user signals
You need to own your intelligence.
Performance vs. cost curveOSS and closed-source convergencePost-training advantagesScaling AI Businesses: When to Adopt Post-Trained Models
Scale Drives Adoption of Post-Trained Models
Larger user bases make the cost of frontier model inference unsustainable, making the shift to cheaper, post-trained models an existential priority for business viability.
The larger you are, the more existential it becomes to shift that token volume towards open source.
Post-Training Unlocks Latency and Reliability Gains
By fine-tuning models on their own signals, companies achieve greater control, leading to improved latency, reliability, and overall user experience.
Post-Training Workflow with Base 10: From Data to Deployment
The Base 10 Post-Training Promise
Customers define their optimization goal, supply data, and select a base open-source model. Base 10 provides the scaffolding to create a specialized model and seamlessly integrates it into inference, abstracting away all complexity.
Post-Training Workflow
- Define your utility function: the metric you want to optimize (e.g., minimize transcription errors).
- Provide a dataset relevant to your use case.
- Choose a base open-source model (e.g., Kimmy K25).
- Base 10 supplies the scaffolding to post-train a specialized model.
- The model is automatically integrated into Base 10’s inference stack for deployment.
They come with data and what they know about their workflows, and they leave with a post-trained specialized model running on Base.
Multi-cloud capacity managerBaseten Inference StackModel API and TrainingTrust, Security, and the Open Source Imperative
Trust Through Strict Security
Base 10 maintains an incredibly intense security posture, setting up internal boundaries to prevent data leakage between competitors, thus earning customer trust.
Open Source as National Security
Open source models are a matter of U.S. national security; the best currently come from China, and if intelligence is 70–90% cheaper in the East, that’s a bad outcome for America.
Anthropic’s U.S.–China AI Scenarios
We think intelligence shouldn't be owned by two people.
Hardware Landscape: NVIDIA Dominance and the Rise of Heterogeneous Chips
NVIDIA Dominance in Inference
The majority of inference workloads run on NVIDIA GPUs, propelled by a mature supply chain, strong TSMC relationship, and low cost of capital, making it extremely difficult for competitors to match scale today.
The Shift to Heterogeneous Architectures
New chips are separating the two core parts of inference—prefill (compute-bound) and decode (memory-bound)—onto different specialized hardware, moving away from the traditional single-chip approach.
there's nothing like CUDA like CUDA is insane.
Compute Scarcity and the Rent-vs-Own Pivot
Stitching Compute from 20+ Clouds
Base 10 pools GPUs from over 20 cloud providers and 87 clusters, making compute fungible. This ensures access to scarce hardware by abstracting infrastructure complexity from customers.
Renting vs. Owning Economics
Why Supply Won't Normalize
Unlike JFK Airport, AI inference has no daily reset. Demand compounds as models grow and apps become agentic, making GPU access a permanent strategic bottleneck.
Future Bets, Student Advice, and Q&A
Next Big Bet: Modular Data Centers
Tuhin would invest in energy and build modular data centers to standardize the unit of compute, similar to how shipping containers revolutionized trade. This would create an API for compute and spur an entire industry.
Advice for Students
Study what excites you; expertise can be built in months. A key emerging skill is project financing for compute infrastructure, given the massive buildout underway.
If the thesis is AGI is everything, every dollar should go to pre-training.