High-performance GPUs are becoming the standard for training modern AI models, but real innovation depends on the infrastructure behind them. At Yotta, we’ve engineered a platform that delivers scalable, consistent, and production-grade performance for demanding AI workloads. To demonstrate its capabilities, we chose Llama 3.1 70B, one of the most trusted benchmarks in the LLM ecosystem, and ran a full training run on a 256-GPU NVIDIA H100 cluster powered by Shakti Bare Metal.
Shakti Bare Metal provides dedicated access to NVIDIA H100 and L40S GPUs with direct hardware control, low-latency performance, and enterprise-grade security. It supports seamless scaling from single nodes to large clusters, making it ideal for AI and HPC workloads.
The Results
We benchmarked our performance against NVIDIA’s published speed of light numbers. Here’s how Yotta’s infrastructure stacked up:
Training Step Time:
– 14.96 seconds per step (vs NVIDIA’s 14.72 seconds)
– 99.5% alignment with reference
FLOPs Utilisation (BF16 Dense):
– 525.83 TFLOPs out of a theoretical 989 TFLOPs
– 53.16% utilisation (vs NVIDIA’s 54.24%)
These were achieved in production on our Shakti Bare Metal platform. This benchmark shows that our infrastructure performs almost identically to NVIDIA’s internal systems under real-world conditions.
How We Got There
Delivering this level of performance is the result of end-to-end system engineering and optimisation. Here’s what powers our performance:
1. High-Bandwidth Interconnects: We used RDMA and NVLink to ensure fast, low-latency GPU communication – critical for scaling deep learning workloads. This architecture minimises latency and maximises bandwidth, ensuring that data flows efficiently across all GPUs – even under heavy load.
2. Advanced Parallelism Techniques: Our setup combined tensor, pipeline, and data parallelism – finely tuned for LLM training using tools like Megatron and DeepSpeed.
3. Intelligent Orchestration Stack: SLURM-based orchestration enabled flexible resource allocation and high availability, with tight runtime controls and minimal scheduling overhead.
Built for What’s Next in AI
Training a model like Llama 3.1 70B is no small feat. It requires vast compute power, precision engineering, and weeks of effort. Our benchmark proves that we can not only handle this scale, but we can also do it with world-class efficiency.
– We’ve trained a state-of-the-art LLM on production infrastructure
– We’ve delivered performance that closely aligns with NVIDIA’s published reference numbers
– We’re ready to support the next wave of AI innovation at scale
Training large language models requires more than powerful GPUs. It demands a tightly optimized, end-to-end system. From compute density and GPU interconnects to orchestration, scheduling, and data pipeline efficiency – every layer impacts how fast you can train, how far you can scale, and how effectively you manage cost.
With Shakti Bare Metal, we’ve engineered a platform built on three foundational pillars designed for real-world AI outcomes:
Performance That’s Proven
We don’t just promise benchmarks – we deliver them. Real workloads, real infrastructure, and numbers that speak for themselves.
Scalability That’s Linear
Whether you’re running on 8 GPUs or 256+, our architecture ensures that performance doesn’t fall off a cliff as you scale.
Value That Scales With You
We combine bare metal efficiency, transparent pricing, and hyperscaler-grade support – so you can grow without unexpected costs or hidden complexity.
AI Builders, This Is Your Platform
For teams building frontier models, enterprise copilots, or domain-specific LLMs, Yotta offers an infrastructure layer that’s ready for tomorrow. These benchmarks confirm that our systems can match the best in the world – giving you the foundation to innovate faster, scale smarter, and stay ahead.
And we’re not stopping here. We’ve got NVIDIA B200 GPUs on the way, further expanding our capabilities to support next-gen AI workloads with even greater efficiency and scale.
Whether you’re in finance, healthcare, manufacturing, or AI research, the time it takes to train a model, the cost per run, and the throughput of your infrastructure all determine your speed to impact. With Yotta’s Shakti Cloud, you don’t have to compromise.