GitHub released a repository-growth endpoint on September 4, 2026, with an unusual bargain: less personal data, better analytics. This GitHub star history API tutorial shows how to fetch that data, turn the weekly buckets into chart-ready rows, and avoid the…
A 770-billion-parameter model sounds like a brute-force scaling story. The Tencent Hy4 architecture tells a more interesting one: its backbone activates 49 billion parameters for each token, then keeps narrowing the work through sparse attention, reused token indexes, and speculative…
Hugging Face hosts 2.96 million public model repositories. Yet 1.5% of them account for 99.2% of downloads, according to Hugging Face’s summer report, published August 14, 2026. That is less a long tail than a comet: one dense head, followed…
Unsloth’s Qwen3.8-27B repository contains 21 Dynamic quant files spanning 6.19 GB to 31.46 GB. This Unsloth Dynamic 3.0 GGUF guide turns that menu into a decision: choose the largest quant that leaves room for context and runtime overhead, then test…
The official Qwen3.6 27B GGUF conversion showed 79,877 downloads when checked on August 17, 2026. That is serious attention for a model whose smallest official quantization is still a 19.1 GB download. Qwen packed coding, reasoning, and vision into that…
If you are asking how much VRAM for a 30B LLM, the tempting answer is “about 16 GB at 4-bit.” That answer is tidy, memorable, and unreliable. One current 30B Q4 file is 14.79 GiB; another is 23.75 GiB. Neither…
Nemotron 3.5 Lightning activates 3 billion parameters per token. Its Q4 GGUF is still nearly 24 GiB. If you want to run Nemotron 3.5 Lightning locally, that distinction determines whether you get a useful server or a very sophisticated out-of-memory…
The 4-bit Muse Glimmer main file is 15.88 GB. That sounds like an 18 GB local model until the vision projector, KV cache, runtime buffers, and optional DFlash drafter arrive with their own invoices. If you want to run Muse…
Z.ai’s GLM-5.2 arrived in mid-June 2026 with the kind of spec sheet that makes coding-agent people briefly forget their backlog: 1 million tokens of context, 128K maximum output, OpenAI-compatible API calls, tool use, streaming, structured output, MCP support, and official…
