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    <title>Thoughts — Paymanshu Sharma</title>
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    <description>Essays on machine learning, agents, and engineering by Paymanshu Sharma.</description>
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    <lastBuildDate>Thu, 29 Oct 2026 15:38:03 GMT</lastBuildDate>
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      <title>Fitting Really Big Models into Not-So-Big GPUs: Precision and Quantization</title>
      <link>https://paymanshus.github.io/thoughts/llm-optimization-memory-gpu-cpu/</link>
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      <pubDate>Sat, 14 Dec 2024 00:00:00 GMT</pubDate>
      <description>How much GPU memory a model actually needs and how to cut it down — fp32, fp16 and bf16, what quantization trades away, LLM.int8() and bitsandbytes in practice, and the KV cache that grows on top of the weights.</description>
      <category>llm optimization</category>
      <category>quantization</category>
      <category>precision</category>
      <category>kv cache</category>
      <category>vram</category>
      <category>inference</category>
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      <title>On-Device Large Language Models on Android</title>
      <link>https://paymanshus.github.io/thoughts/on-device-llms-on-android/</link>
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      <pubDate>Sat, 14 Dec 2024 00:00:00 GMT</pubDate>
      <description>Why running LLMs on phones is worth the trouble, and two working Android demos — a RAG document-querying app built on Phi-3-mini and ONNX Runtime, and a multi-LoRA adapter-switching app on Gemma 2B.</description>
      <category>on-device ai</category>
      <category>edge llm</category>
      <category>onnx runtime</category>
      <category>quantization</category>
      <category>android</category>
      <category>rag</category>
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