Mem Reduct trims cached RAM for local LLMs
A Reddit user says they are using Mem Reduct on Windows to free up memory while running Qwen 3.6 35B A3B MXFP4 locally in LM Studio. On an RX 6700 XT 12GB with 32GB DDR4 and an i5-12400F, they report RAM usage dropping from roughly 28GB to around 20-22GB after cleanup, with throughput around 26-32 tokens per second depending on turbo settings. The post reads like an early field test of whether aggressive memory cleanup can help local-LLM workloads feel smoother on limited RAM.
Hot take: this looks useful as a pressure-release valve for Windows, but it is not a real model optimization. It likely trims cached/standby memory, so the benefit is avoiding memory pressure rather than making the LLM itself smaller or faster.
- –The reported numbers are anecdotal and not a controlled benchmark.
- –Mem Reduct is a Windows memory utility, so the gain is probably from reclaiming cache and standby pages, not reducing the model’s true footprint.
- –The post is still relevant for local-LLM users who are trying to squeeze large models into 32GB systems without hitting swap.
- –The more interesting signal is the hardware balance: 12GB VRAM plus 32GB RAM can run surprisingly large quantized models, but CPU thermals and memory pressure become the limiting factors.
DISCOVERED
45d ago
2026-04-17
PUBLISHED
45d ago
2026-04-17
RELEVANCE
AUTHOR
CryptographerTop4354