https://wiki-planet.win/api.php?action=feedcontributions&feedformat=atom&user=Carinekmin 2026-06-01T21:29:55Z User contributions MediaWiki 1.42.3 https://wiki-planet.win/index.php?title=Why_What_Clients_Need_from_Event_Companies_in_Kuala_Lumpur_for_Large_Language_Models_Matters&diff=2008384 2026-05-28T20:42:21Z

<p>Carinekmin: Created page with &quot;&lt;html&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Large Language Models are not small transformer models. GPT-2 has 1.5 billion parameters at its largest. GPT-3 has 175 billion parameters. LLMs require specialized infrastructure. An LLM event is not a standard NLP conference. It should handle parameter scaling, latency reduction, instruction design, external data connection, and responsible deployment strategies.&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Businesses assessing coordi...&quot;</p> <hr /> <div>&lt;html&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Large Language Models are not small transformer models. GPT-2 has 1.5 billion parameters at its largest. GPT-3 has 175 billion parameters. LLMs require specialized infrastructure. An LLM event is not a standard NLP conference. It should handle parameter scaling, latency reduction, instruction design, external data connection, and responsible deployment strategies.&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Businesses assessing coordinators in Klang Valley 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.&lt;/p&gt;&lt;h2&gt; Inference Infrastructure: Serving Billions of Parameters&lt;/h2&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; 175 billion parameters require at least 350GB at half precision. Pipeline parallelism distributes transformer blocks.&lt;/p&gt;&lt;p&gt; &lt;img src=&quot;https://i.ytimg.com/vi/J-kKR3omk-g/hq720.jpg&quot; style=&quot;max-width:500px;height:auto;&quot; &gt;&lt;/img&gt;&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; A coordinator from Kollysphere agency shared: “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 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.”&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Ask event companies in Kuala Lumpur: What hardware infrastructure do you use for inference (GPU type, count, memory).&lt;/p&gt;&lt;h2&gt; Latency and Throughput: Generation Speed Matters&lt;/h2&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Generating 100 tokens can take seconds. Latency affects user experience and interactivity. Throughput is the number of tokens per second.&lt;/p&gt;&lt;p&gt; &lt;iframe src=&quot;https://www.youtube.com/embed/Rqa60NXCPao&quot; width=&quot;560&quot; height=&quot;315&quot; style=&quot;border: none;&quot; allowfullscreen=&quot;&quot; &gt;&lt;/iframe&gt;&lt;/p&gt;&lt;p&gt; &lt;iframe src=&quot;https://www.youtube.com/embed/MZmNxvLDdV0&quot; width=&quot;560&quot; height=&quot;315&quot; style=&quot;border: none;&quot; allowfullscreen=&quot;&quot; &gt;&lt;/iframe&gt;&lt;/p&gt;&lt;p&gt; &lt;img src=&quot;https://i.ytimg.com/vi/GSmKwiUc2mo/hq720.jpg&quot; style=&quot;max-width:500px;height:auto;&quot; &gt;&lt;/img&gt;&lt;/p&gt;&lt;p&gt; &lt;img src=&quot;https://i.ytimg.com/vi/6v18uaoyeHw/hq720.jpg&quot; style=&quot;max-width:500px;height:auto;&quot; &gt;&lt;/img&gt;&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; An ML engineer in KL posted: “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.”&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Talk through with your coordinator: Do you measure throughput (tokens per second, requests per second).&lt;/p&gt;&lt;h2&gt; Why &quot;The LLM Knows Everything&quot; Is False&lt;/h2&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; LLMs do not know your internal documents. RAG retrieves relevant documents from a knowledge base.&lt;/p&gt;&lt;p&gt; &lt;iframe src=&quot;https://www.youtube.com/embed/anefDK30uYU&quot; width=&quot;560&quot; height=&quot;315&quot; style=&quot;border: none;&quot; allowfullscreen=&quot;&quot; &gt;&lt;/iframe&gt;&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Pose these questions to coordinators: Do you illustrate the difference between parametric knowledge and contextually retrieved information.&lt;/p&gt;&lt;h2&gt; The Difference between &quot;Accurate&quot; and &quot;Plausible but Wrong&quot;&lt;/h2&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; LLMs produce plausible but incorrect outputs. Verification mechanisms are necessary.&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; &lt;a href=&quot;https://www.4shared.com/office/QRvDNgF-ge/pdf-23718-21771.html&quot;&gt;corporate event planner&lt;/a&gt; recommends showing how LLMs can be wrong even when confident.&lt;/p&gt;&lt;/html&gt;</div>

Carinekmin