Mar 25 · 5 min read · One weekend in my hostel room, I found myself spiraling into a question that felt both obvious and oddly underexplored: if data is everything, and memory is the channel through which intelligence flow
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Mar 19 · 7 min read · GLM-4.7-FP8 is one of the latest models focused on this new generation of developer-centric AI. Developed by Z.ai, GLM-4.7 introduces improvements in agentic coding, reasoning, and tool usage, while t
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Mar 14 · 17 min read · TLDR: There is no single "best" LLM quantization. You classify and choose quantization along three axes: when you quantize (timing), what you quantize (scope), and how values are encoded (mapping). In practice, most teams start with weight quantizati...
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Mar 12 · 15 min read · TLDR: This is a practical, notebook-style quantization guide for Google Colab and Hugging Face. You will quantize real models, run inference, compare memory/latency, and learn when to use 4-bit NF4 vs safer INT8 paths. 📖 What You Will Build in Thi...
Join discussionMar 12 · 15 min read · TLDR: GPTQ, AWQ, and NF4 all shrink LLMs, but they optimize different constraints. GPTQ focuses on post-training reconstruction error, AWQ protects salient weights for better quality at low bits, and NF4 offers practical 4-bit compression through bit...
Join discussionMar 8 · 13 min read · TLDR: Quantization converts high-precision model weights and activations (FP16/FP32) into lower-precision formats (INT8 or INT4) so LLMs run with less memory, lower latency, and lower cost. The key is choosing the right quantization method for your a...
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Feb 10 · 6 min read · The year is 2025, and the landscape of AI development has dramatically shifted. Next-gen processors equipped with Neural Processing Units (NPUs) are now ubiquitous, from smartphones to industrial IoT. This paradigm shift demands a new approach to opt...
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Nov 3, 2025 · 7 min read · PurposeBuild a reusable service that takes only a Hugging Face model_id (plus optional tuning parameters) and produces optimized artifacts for multiple deployment targets. The service should be usable by humans, agents, or external tools (VS Code, Cl...
Join discussionOct 10, 2025 · 4 min read · “The more you understand how the machine works, the smarter your questions become.” 🧰 Part 1: The Engine Room — Why AI Needs a GPU, Not Just a CPU When you run AI models, your computer’s “brain” plays a big role. But not all brains are built for th...
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Sep 15, 2025 · 6 min read · From Stub to Coherent MLX Model, a story-shaped release note Getting Meta's efficient 950M parameter model running natively on Apple Silicon for fast, local inference I’ve done a tiny bit of contributions to the llama.cpp community, including adding ...
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