Proven Performance: Up to 250X Faster Than Open-Source Solutions
Independent lab testing with rigorous benchmarks on NVIDIA H100 clusters demonstrates IRONBYTE's superior performance across all AI/ML workloads.
Llama-2-7B pretraining on 2-node cluster: 5 minutes vs 492 minutes
Llama-2-7B fine-tuning on 2-node cluster: 15 minutes vs 3,750 minutes
Multi-inference token generation: 0.0019s vs 0.02s per token
Parameter #1
Independent testing conducted on enterprise-grade NVIDIA H100 GPU clusters
Parameter #2
Standardized methodology comparing IRONBYTE vs. open-source solutions
Parameter #3
Real-world workloads: LLM pretraining, fine-tuning, and inference
Parameter #4
Industry-standard models: Llama-2-70B and Llama-2-7B
Infrastructure Details
Hardware: NVIDIA H100 GPUs (80GB) in 1-node and 2-node cluster configurations
Network: 10 Gbit/s and 80 Gbit/s network connectivity
Environment: AlmaLinux 9.5, NVIDIA drivers 550+, CUDA 12.2+
Containerization: Docker with pre-loaded ML libraries and frameworks
Testing Methodology
Workload Standardization: Identical computational tasks across pretraining, fine-tuning, and inference
Dataset Consistency: fineweb-edu (10BT) and databricks-dolly-15k for reproducible results
Performance Monitoring: Real-time resource tracking with nvtop and bmon utilities
Business Impact of Performance Gains
Achieve multiple more output from existing GPU investments.
Deploy AI models weeks or months sooner.
Faster training means dramatically lower electricity and hardware costs.
Train larger models or serve more customers with same resources.
Ready to 2X Your GPU Performance?
Request Consultation →
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