How do we benefits from 2+ T models?

<!-- SC_OFF --><div class="md"><p>Running Llama-3-70B with long contexts and hitting HBM limits constantly. 128K context burns approx 40GB just for KV cache — half the H100 gone

<!-- SC_OFF --><div class="md"><p>Went down the local LLM rabbit hole. Looked at P40s, V100s (almost bought an SXM2 version that doesn’t even plug into a normal motherboard lmao),

<!-- SC_OFF --><div class="md"><p><a

<!-- SC_OFF --><div class="md"><p>Got Qwen 3.5 27B to 1,103,941 tok/s on 12 nodes with 96 B200 GPUs.</p> <p>At that rate you process 50,000 insurance policy documents in hours

<!-- 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

<h2> FlashQLA Kernels Accelerate AI; NVIDIA &amp; AMD Unveil New GPUs </h2> <h3> Today's Highlights </h3> <p>This week, Qwen introduced FlashQLA, high-performance attention

<!-- SC_OFF --><div class="md"><p>Today I set up a full coding toolbox on a single RTX 5080 (with RAM offloading) that's actually viable.</p> <p><strong>Autocomplete</strong>:

<!-- SC_OFF --><div class="md"><p>Every output token the model generates requires reading the entire KV cache from High Bandwidth Memory.</p> <p>Here's what that actually looks