Homelab user runs 2.8T-parameter Kimi K3 on CPU with Optane PMem 200

according to the published report, a homelab builder on Reddit ran Moonshot AI’s 2.8-trillion-parameter Kimi K3 model using only a CPU and second-generation Optane PMem 200 memory.reported The system avoided graphical processing units entirely.A hobbyist successfully ran a massive AI model on standard computer memory without specialized graphics chips.The implementation utilizes Optane PMem 200 in App Direct Mode to bypass GPU constraints for 2.8T-parameter inference. The builder used a 40-core 3rd-gen Xeon Scalable processor. The setup required dual 12W blower fans aimed at the memory modules to control temperatures.reported The creator stated that active DIMM cooling is mandatory for this configuration.

The figures

Tom’s Hardware cited the following metrics from the Reddit post. The model has 2.8 trillion parameters.reported The inference speed is 0.85 tokens per second with 1-bit quantization.reported The speed drops to 0.75 tokens per second with 3-bit quantization.reported The smallest community quantization requires at least 610GB of RAM. Intel launched Optane PMem 200 in 128GB, 256GB, and 512GB capacities.reported

Behind the figures

The performance data comes from a user’s personal homelab experiment documented on Reddit. Tom’s Hardware summarized the technical details. The builder measured tokens per second during inference. The thermal requirements were determined by monitoring module temperatures under load. The cost comparison was based on the builder’s assessment of secondhand Optane prices versus current DRAM market rates.

What it means for your stack

Moonshot AI recommends data center inference engines for Kimi K3.reported The model is not designed for home use. Standard DRAM prices have risen sharply. The builder noted that Optane memory costs a fraction of equivalent DRAM capacity. This makes secondhand Optane one of the few ways to reach the required 610GB memory threshold at home.reported

Building on this

Developers can use Optane PMem 200 in App Direct Mode to expose the memory as a separate persistent region.reported This allows software to map tensors directly. The previous build’s creator, u/APFrisco, noted this offers more control over tensor placement. Performance is significantly slower than DRAM. The second-gen Optane delivers up to 32% more bandwidth than the first generation.reported Overnight batch processing may tolerate the low throughput.