Inference-ready GPU infrastructure is now live in Sweden, delivered in partnership with Conapto. Built for low-latency workloads and aligned with EU compliance. Deploy a GPU cluster in Sweden: https://t.co/5uxlbVZ5sP

When latency and compliance don’t dictate location, cost becomes the real constraint. As @cudopete points out, the decision often comes down to total cost of ownership, not headline GPU pricing. At scale, optimization keeps costs predictable rather than compounding.

Efficient #AI training is available to everyone. Profile workloads, right-size GPUs, schedule smartly, and run sustainably. Learn more about pressure-testing your AI training assumptions:

Case study: JetMoE-8B achieved Llama-class performance under $100k. Sparse activation and precision-aware training drove efficiency at scale.

Total cost of training ownership (TCTO): Compute + Energy + Storage + Engineer hours + Failed runs. Small gains in precision and scheduling shrink every variable.

Real levers for efficiency: • Mixed precision FP16–FP8 • Optimized batch scheduling • Compiler acceleration • Carbon-aware regions Together, they reduce runtime, emissions, and cost.

Perception 5: One benchmark defines performance. Reality: MLPerf and Hugging Face results show that precision and sparsity tuning cut. FLOPs 30–60% with only 1–2% accuracy loss.

Perception 4: Spot instances always save money. Reality: 5–20% eviction rates add hidden costs. Mix spot and on-demand capacity and checkpoint aggressively.

Perception 3: Faster training burns more power. Reality: NVIDIA Blackwell trains 4x faster and 25x more energy-efficient than Hopper. Speed and sustainability now coexist.

Perception 2: Only on-prem stays economical. Reality: Cloud GPU instances now deliver over 80% better price-performance than fixed clusters. Flexibility, not ownership, drives efficiency.

Perception 1: Bigger models mean higher cost. Reality: JetMoE-8B trained on 96 H100s for under $100k and beat Llama 2 7B. Sparse design and smarter compute redefine the cost curve.

The truth about efficient #AI training is that many still think only hyperscalers can afford real efficiency. But public benchmarks, new GPUs, and smarter software prove otherwise. Follow this thread to see what the data shows.

You don’t get to opt out of scale in #AI. Demand keeps coming. As @CudoPete puts it, the real decision isn’t whether you scale, but how early you engineer for it. Optimization is what keeps scale from becoming a liability.

Efficiency is engineered. This is what high-performing teams do differently: • Profiling • GPU right-sizing • Using mixed capacity types • Compiling critical paths • Choosing renewable regions Train smarter & deploy faster with CUDO Compute: https://t.co/TnhrBPdgyx

The idea that the cloud always costs more is broken. Modern GPU clouds now outperform on-prem for price-performance, flexibility, and time to outcome. When power, land, and latency matter, the cloud wins. Read the full breakdown: https://t.co/TnhrBPcIIZ

#AI teams are hitting hard limits: power, land, thermals, and lead times. Those treating infrastructure as a last-mile decision are stalling out. The ones scaling now? They planned for it. As @CUDOPete says, operational-grade GPU infrastructure is the difference. We deliver it.

#AI training efficiency is no longer hyperscaler territory. MLPerf v5.0 shows a 2x+ uplift in core workloads, proving this is purely an engineering challenge. Hardware, precision, & scheduling now outperform budget size. Here's our complete breakdown: https://t.co/TnhrBPdgyx

Design #AI workloads for production: • Benchmark real pipelines • Track cold-start vs steady-state • Include vector store latency Red flags: • GIL contention • Blocked cores • Missing async • Prompt sprawl • Lost traces We break it all down: https://t.co/wmAZm2lFjR

Hybrid cloud and sovereign AI were front and center at @HPE Discover Barcelona. Barry Kick from CUDO Compute was in conversations grounded in production, not theory. The shift was clear: #AI infrastructure is now about deployment, latency, & readiness. See you at the next one!