The Agenda of What Clients Need from Event Companies in Kuala Lumpur for Large Language Models

2026-05-28T20:27:23Z

Harinnpvmz: Created page with "<html><p class="ds-markdown-paragraph" > LLMs operate at a different scale. BERT-base has 110 million parameters. GPT-3 has 175 billion parameters. LLMs need multiple GPUs or TPUs. A large language model summit differs from a BERT fine-tuning workshop. It needs to cover compute requirements, model compression, input crafting, knowledge base integration, and ethical deployment.</p><p class="ds-markdown-paragraph" > Organizations reviewing planners across the capital for..."

<html><p class="ds-markdown-paragraph" > LLMs operate at a different scale. BERT-base has 110 million parameters. GPT-3 has 175 billion parameters. LLMs need multiple GPUs or TPUs. A large language model summit differs from a BERT fine-tuning workshop. It needs to cover compute requirements, model compression, input crafting, knowledge base integration, and ethical deployment.</p><p class="ds-markdown-paragraph" > Organizations reviewing planners across the capital for large language model events|for LLM summits|for foundation model gatherings need specific technical capabilities|must address particular infrastructure requirements|should cover deployment and optimization strategies.</p><p> <iframe src="https://www.youtube.com/embed/eyxmSmjmNS0" width="560" height="315" style="border: none;" allowfullscreen="" ></iframe></p><h2> The Difference between "Running an LLM" and "Serving an LLM at Scale"</h2><p class="ds-markdown-paragraph" > 175 billion parameters require at least 350GB at half precision. Model parallelism splits layers across multiple GPUs.</p><p class="ds-markdown-paragraph" > An experienced event planner in Kuala Lumpur explained: “A vendor claimed an LLM demo. They used GPT-2. 'That is not an LLM,' I said. 'GPT-2 has 1.5 billion parameters maximum. Modern LLMs are 100 times larger.' 'We can <a href="https://www.inkitt.com/celeenflgz">company event management</a> scale up,' they said. 'Do you have multi-GPU infrastructure?' I asked. They did not. They were using a small model and calling it large. Now we verify model size and infrastructure in every LLM event.”</p><p class="ds-markdown-paragraph" > Inquire with planners: Do you demonstrate model parallelism or tensor parallelism for serving the LLM.</p><h2> The Difference between "Works" and "Works at Production Speed"</h2><p class="ds-markdown-paragraph" > LLM inference is slow. Latency is the time to generate a response. Throughput is the number of tokens per second.</p><p> <img src="https://i.ytimg.com/vi/hZ4a4NgM3u0/hq720.jpg" style="max-width:500px;height:auto;" ></img></p><p class="ds-markdown-paragraph" > One client shared: “I attended an LLM event where the presenter generated short responses. Fast. I asked 'what is the latency for a 500-word response?' They had not measured. We tested. It took 45 seconds. 'Can you serve 100 concurrent users?' I asked. They did not know. They had not considered production constraints. Now I ask for latency and throughput numbers explicitly.”</p><p class="ds-markdown-paragraph" > Review with your planner: Do you measure throughput (tokens per second, requests per second).</p><p> <iframe src="https://www.youtube.com/embed/GSmKwiUc2mo" width="560" height="315" style="border: none;" allowfullscreen="" ></iframe></p><h2> Why "The LLM Knows Everything" Is False</h2><p class="ds-markdown-paragraph" > LLMs know only what was in their training data. RAG enables question answering over private data.</p><p class="ds-markdown-paragraph" > Inquire with planners: Do you show how to connect an LLM to a private knowledge base (documents, databases, websites).</p><h2> Hallucination Management: Knowing When the LLM Is Wrong</h2><p class="ds-markdown-paragraph" > LLMs hallucinate. Hallucination detection is critical.</p><p> <img src="https://i.ytimg.com/vi/zgDZew7DHPc/hq720.jpg" style="max-width:500px;height:auto;" ></img></p><p class="ds-markdown-paragraph" > Professional LLM event planners suggest demonstrating the difference between a well-grounded response (with retrieval) and a hallucinated response (without retrieval).</p><p> <img src="https://i.ytimg.com/vi/AzlcgUU0IXc/hq720.jpg" style="max-width:500px;height:auto;" ></img></p></html>

Wiki Spirit - User contributions [en]

The Agenda of What Clients Need from Event Companies in Kuala Lumpur for Large Language Models