By cutting the GPU count in half and augmenting with Memstak's Specialty Memory, the performance profile shifts
The 4-GPU cluster with Memstak is superior in almost every metric. It provides lower latency, higher effective throughput, and uses 50% less power. The only scenario where the 8-GPU cluster might win is in massive "Offline Batching" where latency doesn't matter and you can saturate all 8 GPUs with millions of simultaneous requests.
Where Memstak Makes the Biggest Difference
Generative AI Inference
When an entire model fits within proximity stacked memory, the compute pipeline never stalls.
Real time AI
Code generation
Document analysis
Cloud inference platforms
AI Model Training
Hiding memory latency keeps Tensor Cores computing instead of waiting, yielding 1.5 to 2x faster training.
Pre-training
Fine tuning & RLHF
Continual learning
Budget constrained research
Total Cost of Ownership
Half the GPUs, comparable throughput, dramatically lower operating costs
Hyperscale cloud
Enterprise clusters
Colocation
Edge deployments
Economics broken down
Memory Cost Savings
HBM and CoWoS packaging consume 60 to 80% of GPU bill of materials in leading accelerators. Memstak projects cost reduction by a factor of 5-10. At hyperscale, aggregate memory savings alone reach hundreds of millions per deployment.
Power Savings
US data centers draw ~41 GW today, up 150% in five years. Memstak equipped clusters consume an order of magnitude less energy per memory access, and a 4 GPU Memstak cluster matches or exceeds an 8 GPU standard configuration, halving power, cooling, and infrastructure costs.
By 2030, 50-90 new nuclear plants may be needed just to power AI data centers. Memstak's efficiency gains could significantly reduce that number
Can We Cut AI Data Center Cost in Half?
The industry is projected to spend $700 billion on AI infrastructure by 2026. The single largest line item in that spend is memory and its associated thermal management. When you reduce memory BOM by an order of magnitude, cut power consumption per access, and halve the GPU count required for equivalent throughput, the total cost implications are transformative.
It would be great if AI could be more affordable for everyone. That ambition drives everything we build.
How Memstak Compares to HBM
Discover Your Next Gen Performance Multiplier
Explore how our proprietary stacked cache can improve your throughput at a lower cost.
Whether you are evaluating memory alternatives for a next generation accelerator or optimizing an existing deployment, our engineering team is available for technical discussions and detailed performance projections tailored to your workload.
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