DeepSeek V4 Flash Runs on 8GB RAM With No GPU

Running a 79 GB AI model on a machine with 8 GB of RAM and no GPU sounds impossible. DeepSeek V4 Flash just did it using a memory-mapped GGUF file on an NVMe SSD.

What Happened

DeepSeek V4 Flash has 284.33 billion parameters and ships as a 78.62 GiB GGUF quant. The test rig used 8 GB RAM, CPU-only processing, and an NVMe SSD — no CUDA, no GPU. Linux mapped the GGUF file into virtual memory and used demand paging to load only the active weights into RAM. The result: a first-token diagnostic at around 5.33 seconds, with a resident set size of about 5.9 GiB and no process swaps.

Why It Matters

This setup does not promise chat-speed performance. The developer says sustained decoding speeds are not assured, and the 5.33-second number comes from a controlled first-token test, not real conversation latency. Still, it shows that a model too big for RAM is a performance problem, not a hard wall. As smart paging, MoE expert loading, and faster NVMe drives improve, SSDs will become a real part of the local AI memory stack alongside VRAM and RAM.