Serving several chat LLMs from one 16 GB RTX 5080 by reusing an existing router + ~150 lines of shell — with controlled benchmarks: where memory split doesn't matter, where prefix caching doubles throughput, and where 'smart' routing made things slower.
Every number below was measured on a single RTX 5080 (16 GB) and is reproducible
from the repo. Each result states the exact config it was measured under; I don't
compare numbers across configs, and I flag anything we did **not* cleanly measure.
TL;DR
You can serve several small chat LLMs from one 16 GB RTX 5080, behind a single
The same 16 GPUs, twice the users: Inference-aware routing for LLM clusters
768GB of cheap Intel Optane DIMM memory sticks used to run 1-trillion-parameter LLM on a system with a single GPU — local Kimi K2.5 install achieved roughly 4 tokens per second
ZFLOW AI's Simulation-Guided Optimization Identifies a 1.54× Higher-Throughput Serving Configuration for DeepSeek V4-Pro on 8×B300
Learn how inference-aware routing can double your large language model (LLM) cluster's capacity while keeping your GPU bill flat.…
How to go from 86 GiB idle VRAM (instant OOM) to 0 GiB idle / 40 GiB peak by using a cold-start design for LTX-2.3 on one RTX Pro…
Did you know that a 35-billion-parameter model can generate tokens at the same compute cost as a 4B...
TL;DR — 6 machines, 12 GPUs, 1,000+ concurrent agents, P95 18 ms, voice <300 ms, 280,741 lines of...
Redditor found 768GB of affordable Optane sticks second-hand.
ZFLOW AI's Simulation-Guided Optimization Identifies a 1.54× Higher-Throughput Serving Configuration for DeepSeek V4-Pro on 8×B300
