Generative AI (GenAI) isn’t just for the cloud anymore. More enterprises are bringing it in-house — especially for small to midsize workloads where cost, performance, and data privacy are top priorities.
In most cases, on-prem GenAI focuses on retrieval-augmented generation (RAG), inference, or small-scale fine-tuning — not massive LLM training. That makes it both feasible and cost-effective.
Start with the use case — define whether you’re fine-tuning a model, running high-volume inferences, or adding enterprise-specific data. For lighter workloads, AI-optimized CPUs, flash storage, and modern Ethernet may be enough.
Key infrastructure choices:
Compute: CPUs, cost-friendly GPUs (AMD, Intel Gaudi), or custom AI chips for niche needs.
Storage: Flash-based solutions with strong data management capabilities.
Networking: InfiniBand for high performance; modern Ethernet with RoCE for smaller, manageable setups.
The trend: Gartner predicts on-prem AI workloads will grow from under 2% in 2025 to over 20% by 2028.
Takeaway: On-prem GenAI isn’t about replicating the cloud — it’s about matching infrastructure to your needs for maximum impact.
🔗 Read the full guide: How to Plan On-Prem Generative AI Infrastructure[https://www.aptlytech.com/how-to-plan-on-prem-generative-ai-infrastructure/]
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