LLM performance decreased significantly over time using the same models and same

<!-- SC_OFF --><div class="md"><blockquote> <p><strong>WARNING: wait before download from HF: I just realised my upload of the new versions with the additional fix in the chat

<!-- SC_OFF --><div class="md"><p>I have experience using llama.cpp on Windows/Linux with 8GB NVIDIA card (384 GB/s bandwidth) and offloading to CPU to run MoE models. I typically

<!-- SC_OFF --><div class="md"><p>How do you guys deal with long context, for example while coding, when you’re going back and forth for adjustments or fixing some errors and

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1srhtvg/where_we_are_in_a_year_everything_has_changed/"> <img alt="Where we are. In a year, everything has

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1tbzr64/qwen36_just_stops/"> <img alt="qwen3.6 just stops"

<!-- SC_OFF --><div class="md"><p>I read this sub every day and I keep seeing benchmarks and discussions focused almost entirely on tokens/s generation speed. Prompt processing

<!-- SC_OFF --><div class="md"><p>That foodtruck bench post showing deepseek v4 matching gpt-5.2 at 17x cheaper got me thinking. if frontier cloud models are that overpriced for

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1t4uj9h/mtp_on_strix_halo_with_llamacpp_pr_22673/"> <img alt="MTP on strix halo with llama.cpp (PR #22673)"