SF Tensor recently launched!

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^{"Building the future of high-performance compute"}

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^{TL;DR: SF Tensor lets AI researchers forget about the infrastructure layer and focus on their research. They automatically optimize kernels to run faster, find the cheapest GPUs across every provider and migrate your jobs when spot instances fail. Training AI should be about AI, not DevOps.}

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Founded by Ben Koska, Luk Koska & Tom Koska‍

Three brothers that have been working on Artificial Intelligence together for years, most recently training their own Foundational World Models. SF Tensor was born out of their own needs as AI researchers scaling up training runs to thousands of concurrent GPUs.

Ben has been publishing AI research since high school, solo-training models across 4,000 GPUs as co-PI on a 6-figure grant.

Tom and Luk (twins btw) have been doing AI research for years, from starting college in parallel to high school at age 14 to finishing their BSc in CS (at age 16).

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The Problem

Training AI should mean developing smarter architectures and finding better data. But right now, it doesn’t. Teams waste their time on everything but actual research:

• Optimizing code so that training runs don’t drain the bank
• Fighting cloud providers and scrambling for GPU availability
• Making distributed training work with reasonable MFU (=cost efficiency).

This drives up costs, frustrates everyone and kills velocity. Infrastructure has inadvertently turned into the limiting factor for AI research labs, and it’s killing progress.

They experienced this first-hand developing their own foundation models – what they expected to be AI research, experimentation and iterative improvement turned out to be an ugly mix of writing CUDA, debugging driver mismatches and optimizing inter-GPU collective operations. That’s why they decided to solve the infrastructure layer, to allow other researchers to focus on research, not infrastructure.

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Their Solution

SF Tensor is the "set it and forget it" infrastructure layer for anyone training or fine-tuning AI models. Hook up your repo, pick your GPU count and budget, and they deal with the rest:

• Their automatic kernel optimizer analyzes your architecture and tunes execution for any hardware (NVIDIA, AMD or TPUs). No more having to drop down into custom CUDA because PyTorch doesn’t understand memory topology.
• They find the cheapest available compute across all clouds for your specific requirements and launch your training run.
• Automatic Distributed Training allows you to scale from 1 to 10,000 GPUs without having to change your code or killing your MFU
• Everything else that you shouldn’t have to think about: Spot instance migration? Handled. Monitoring? Baked in. Logs and artifacts? Done.

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Compute should be boring. Let’s make it boring.

Try them right now at sf-tensor.com or contact them at hello@sf-tensor.com to see how they can help with your infra pains.

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Learn More

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^{🌐 Visit sf-tensor.com to learn more.}

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^{👉 Ask: Know anyone training or fine-tuning AI models? They would be grateful for an intro! Reach out to the founders here.}

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^{⭐ Give SF Tensor a star on Github.}

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^{👣 Follow SF Tensor on LinkedIn & X.}

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