Large Language Models (LLMs) like Meta’s LLaMA 3.3 with 70 billion parameters have showcased exceptional natural language capabilities, but running them demands immense computational resources. These LLMs require around 160-180 GB of GPU memory, which exceeds what a single GPU can handle, necessitating distributed setups involving multiple high-end GPUs like NVIDIA A100 or H100. For effective service, especially with 100 concurrent users, robust infrastructures are essential to ensure both sub-second latency and resource management.
Costs for such setups can reach hundreds of thousands of euros for on-premise infrastructure, while cloud solutions, despite being more flexible, can still exceed tens of thousands monthly. Privacy regulations like GDPR and the new AI Act present challenges for companies that must manage sensitive data, often making on-premise solutions more compliant. Moving forward, the focus should be on developing more efficient, sustainable models that offer similar performance with reduced energy demands.
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