Performance When Offloading Large Models to System RAM?

<!-- SC_OFF --><div class="md"><p>Hey, </p> <p>Tldr: use local models like qwen 3.5 quantized with proprietary models for fire and forget work. Local model doing the grunt work.

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

<!-- 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>I could fit the Q3 model not sure if it's worth over 27B ? </p> </div><!-- SC_ON --> &#32; submitted by &#32; <a

<!-- SC_OFF --><div class="md"><p>Currently using glm 4.7 flash, it’s very meh </p> <p>Heard omnicoder or Crow 9b are good, are they any better?</p> <p>Or Qwen3.5 27b?</p>

<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>Noob here, Running Qwen3.6 35B A3B in LM Studio on a 3080 10GB + Ryzen 5 3600 on Windows 10.</p> <p>Tried some unsloth quants with identical

<!-- 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