6d ago · 6 min read · If you've been running local LLMs, you already know the drill: download a 70B model, quantize it to 4-bit with GPTQ or GGUF, cross your fingers, and hope your GPU doesn't catch fire. It works. It's practical. But there's a fundamentally different app...
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Apr 6 · 11 min read · Part 1 of 2. Why We Did This Hammer.ai runs a industrial research lab hyper focused on regulated domain document understand at extremely efficient margins. Private equity self funded companies like f
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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 · 16 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 · 14 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 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 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...
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