DeepSeek V4.1 Flash can run at home on 64GB, but speed is the open question

What Unsloth released

Unsloth, a group that has been tuning DeepSeek V4.1 Flash for local use, has put out a new build called Dynamic V3.1. It is a quantized version, meaning the model's numbers are stored at lower precision so the file takes less memory.

Unsloth says this build can run on devices with as little as 64GB of memory. It also says the build performs better than the previous one, finishes tasks faster and gives shorter outputs.

The claims on speed and scores

According to the figures Unsloth published, Dynamic V3.1 ranks first on five benchmarks, which are standardized tests for AI models. They cover reasoning, math, coding and general knowledge.

On speed, Unsloth says its desktop app combined with llama.cpp, a program for running models on ordinary computers, gets about 3x faster DeepSeek V4.1 inference than other repositories focused on CPU, VRAM or n-gram. These are Unsloth's own numbers, and we have not seen them independently checked.

Why MyDrivers is skeptical

As MyDrivers reported, Unsloth's write-up says 64GB works but gives no specific test results for that setup. The outlet also notes that even the smallest quantized version, called IQ1_S, needs well over 64GB, so it is unclear how the 64GB claim works.

One possible route is the model's design. Part of it, the n-gram storage, can sit on an SSD (fast flash storage) instead of in memory. But that would slow things down a lot.

MyDrivers concludes that even if 64GB works, the speed probably would not make it worth using. It suggests at least 128GB for a reasonable experience, though even that would not be fast.

What to check before you try it

Local use is attractive because there is no per-token charge. That is still not free: you need the hardware, and you spend your own time on setup.

Unsloth's website has the full description and deployment steps. Read those first to see exactly what type of memory and what hardware the 64GB figure assumes.