Without Strong Compute this would have taken 2 full time engineers 3-6 months.

Before

On-premises compute hardware limited to 5 NVIDIA GPUs
Slow job migration and deployment between cloud providers
Limited visibility into resource utilization
High operational overhead managing compute resources

After

On-premises compute hardware limited to 5 NVIDIA GPUs
Slow job migration and deployment between cloud providers
Limited visibility into resource utilization
High operational overhead managing compute resources

44 experiments run across 6 separate AI projects - 23 rapid iteration experiments, 21 long-run training experiments
6.5 hours total training time on 256 GPUs in 90 cloud machines across 3 different cloud providers - including H100 and A100 instances

Challenge: Complex AI workloads, scarce hardware

LayerJot, a cutting-edge med-tech startup in Belmont, CA, faced a critical challenge common to AI-driven research teams: managing complex, compute-intensive workloads across multiple datasets and models.

LayerJot’s projects span:

Computer vision for medical equipment catalog processing
Multi-modal AI models like CLIP and Llama
Generalist robot policy models for surgical equipment handling

Solution: Scaling from 5 to 256 GPUs with zero dev-ops

Strong Compute deployed an AI engineer on-site with LayerJot for a full week, working shoulder-to-shoulder with their team to optimize infrastructure and accelerate their AI workloads using the Strong Compute Instant Super Computer.

Technical Deep Dive: Datasets and Model Adaptation

Data Ingested

Models Adapted

On-Site Collaboration: Beyond Infrastructure Management

For one intensive week, Strong Compute embedded an AI engineer directly at LayerJot’s Belmont, CA office. Our engineer worked side-by-side with LayerJot’s team, providing:

Real-time infrastructure optimization
Hands-on model adaptation support
Direct troubleshooting of complex AI workload challenges
Custom infrastructure configuration tailored to LayerJot’s unique research needs

Key Outcomes

Resolved Dense Encoder code base issues and successfully ran experiments
Adapted CLIP-style model for Strong Compute checkpointing
Successfully trained VLA Robotics repo in interactive containers
Integrated model checkpoints from ingested datasets
Demonstrated Claude Code’s capability to adapt complex legacy code bases for training on Strong Compute!

Breakthrough Results

Performance Metrics

Reduced job deployment time from hours to minutes
60GB/sec inter-cloud data transfer speed
7.8-second container launch times

Operational Impact

Resolved complex code base integration challenges
Enabled continuous experiment-based training
Simplified multi-provider infrastructure management

Quote from the Customer

“Strong Compute transformed how we think about infrastructure. It’s not just a tool; they are a strategic partner in our AI development.” - Soren Harner, CEO, LayerJot

Looking Forward

LayerJot is now positioned to:

Scale AI research more rapidly
Reduce infrastructure management overhead
Accelerate medical technology innovation

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Strong Words

Scaling from 5 to 256 GPUs with zero dev-ops…