Unsloth Says 64GB Can Run DeepSeek-V4.1, Official Hardware Table Says 128GB Minimum

Unsloth has released the Dynamic V3.1 quantized GGUF for DeepSeek-V4.1-Flash-1017. The wording in the original post is '64GB of RAM and it takes off.' Looking at the hardware table in the same guide, the 1-bit quantization column says RAM plus VRAM needs 128–145GB, with Engram on SSD. Put the two numbers together, and the difference is the price of a whole machine.

First, let's be clear about the model itself. DeepSeek-V4.1-Flash-1017 is a 748B-parameter MoE model, with a 552B backbone plus 196B of Engram parameters. Engram is conditional memory that works by n-gram table lookup, using combinations of up to four tokens to look up a few rows of table data. The table itself ranges from 110.6GB to 202.8GB and can be placed on SSD and loaded on demand via mmap without taking up VRAM. That is the key to lowering the threshold.

The cost is also direct: every table lookup has to go through the disk. Someone in the comments mentioned that people still using old SATA drives should not get too excited. That's true. Putting Engram on SSD saves memory, but it bets on your disk I/O.

The actual sizes and memory requirements of each quantization version are as follows:

Note that the memory requirements here are estimates. The guide's original wording is 'estimated to allow 5-10GB more than non-Engram weights,' and runtime and context may demand more. The 1M context window is the model's capability upper limit; if you max it out, the KV cache will grow along with the context. The first reply in the comments pointed this out: being able to load with 64GB does not mean being able to max out a 1M context. The original poster also replied, '1M is probably difficult.'

In terms of speed, DeepSeek-V4.1 includes DSpark speculative decoding, which first guesses multiple tokens and then has the main model verify them. Paired with a Q8_0 drafter (8GB), it can make decoding 1.3 to 1.5 times faster. Unsloth has optimized on top of llama.cpp, and the official claim is that it is 3 times faster than similar CPU/VRAM/ngram repositories. This 3x is comparative data, not absolute speed; how fast it actually runs still depends on the disk.

On quantization quality, Unsloth has been in a dispute with the GSQ-RCO quantization released by ISTA-DASLab. The other side claimed 100% accuracy across all quantization bit widths on AIME25, but Unsloth could not reproduce it. Running with 16 samples per question and 30 questions for a total of 480 answers, GSQ-RCO retained 94.64% on average. The two sides are also questioning each other over KLD metrics; Unsloth used multiple held-out test sets to show that its V3.1 has higher KL divergence and top-1 agreement.

There are indeed people on GitHub trying to verify it. In the comments, David says he is running the weights on a 5090 plus 128G plus a Samsung 980 Pro. Someone else asked whether a 4060 8GB plus 64GB of RAM would work; for a model starting at 233GB, that configuration is tight on both memory and VRAM. Others take issue with 1-bit quantization itself, saying Q1 makes an error every 8 to 9 tokens. All of this will have to wait for actual tests before it can be settled.

Back to that 64GB. The guide's original text says 'runs locally on 128GB+ RAM setups or 64GB RAM (slower),' and the 'slower' in parentheses is the key. 64GB is the lower limit for being able to start, not a configuration for comfortable use. If you want to max out 1M context and run faster, you still need to add more memory. Someone in the comments said they are waiting for actual measured speed, and that attitude is right. The model and guide links are below; go look at the hardware table yourself—it's more reliable than marketing language.

Model: https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF

Guide: https://unsloth.ai/docs/models/deepseek-v4.1-flash