What PC for DeepSeek-V4.1-Flash-1017? Memory, machine and license
Updated: November 1, 2026
What PC for DeepSeek-V4.1-Flash-1017?
An 8-card server, not a PC.
DeepSeek-V4.1-Flash-1017 weighs approximately 476 GB in its published form. No single card is enough, nor is a 96 GB 4-card workstation in its native form: you need an 8-GPU server. Its MIT license allows commercial use.
This model appeared on Hugging Face on October 17, 2026. It is a minor update to DeepSeek-V4.1-Flash, according to DeepSeek: a mixture-of-experts model that reads text and images and responds in text. DeepSeek highlights reasoning, coding, and agent tasks. Here is what you need to run it at home, and what we cannot yet state.
The memory it needs
An expert model loads entirely into memory, even if only a part works on each word. The published repository weighs 475.7 GB (native format, reduced precision). This is the safest figure: you need at least this memory, plus the context cache.
| Weight precision | GPU memory | Machine from our range |
|---|---|---|
| Published format (native) | about 480 GB | EPYC 8 GPU AI Server, with optional cards (up to 768 GB). Not the 4-card base configuration (384 GB). |
| Q4 (4 bits) | 316 to 435 GB | EPYC 8 GPU AI Server. The CoreIA Threadripper PRO 4 GPU (384 GB with 4 cards) only if the bottom of the range is correct, which we cannot guarantee. |
| Q8 (8 bits) | 550 to 759 GB | EPYC 8 GPU AI Server with 8 cards (768 GB); at the limit of the top of the range |
| FP16 (16 bits) | 1,032 to 1,426 GB | No machine in our range |
Radiance Systems estimates with a context of 32,768 tokens, without measurement on our machines. The context cache read in the model configuration represents 2.5 GB for this context.
See the EPYC 8 GPU AI Server
See the CoreIA Threadripper PRO 4 GPU
Which machine
| Machine | GPU memory | Price excl. tax | DeepSeek-V4.1-Flash-1017 |
|---|---|---|---|
| CoreIA 2x RTX PRO 6000 | 192 GB | €40,423 | No, it lacks more than 120 GB even in Q4 |
| CoreIA Threadripper PRO 4 GPU | 96 GB base, 384 GB with 4 cards |
€27,206 with 1 card, the extra cards are added |
No in native format. Maybe in Q4, not guaranteed. |
| EPYC 8 GPU AI Server | 384 GB base, 768 GB maximum | €171,791 | Yes, with optional cards for the native format |
It is a server investment, not a desktop workstation. It is justified if you want a model of this level at home, without sending your data to a provider. The server is delivered with the software installed and tested.
Its level, according to DeepSeek
The model sheet compares it to other large models, not to Qwen3.8-27B or Gemma 4 12B: we therefore cannot place it against them with published figures. Here are a few lines from this table. The MiniMax-M3.1-Flash was the subject of another article.
| Test (scores published by DeepSeek) | V4.1 Flash-1017 | V4.1 Flash | Kimi K3 | GLM-5.3 |
|---|---|---|---|---|
| DeepSWE v1.1 (code) | 77.0 | 74.2 | 67.5 | 66.9 |
| Terminal-Bench 4.0 (terminal agent) | 42.3 | 31.2 | 12.6 | 37.9 |
| NL2Repo-Bench (code of an entire repository) | 68.2 | 65.4 | 58.0 | 58.0 |
| Automation-Bench (automation) | 60.5 | 54.8 | 46.7 | 48.8 |
| HLE (without tools) | 42.6 | 36.8 | 43.5 | 42.0 |
| BabyVision (images, with tools) | 92.5 | 89.6 | 85.7 | not published |
Scores given by DeepSeek, measured with its own settings: an order of magnitude, not an independent ranking. On HLE, the score of GLM-5.3 is that of the text subset alone.
The license, primarily for business use
MIT License
The repository is published under the MIT license: commercial use, modification and redistribution are allowed.
A single obligation
Retain the copyright notice and the license text with any copy of the model you redistribute.
Summary of the license indicated on the model sheet, which alone is authoritative. This is not legal advice.
Is a model of this size really necessary?
Often, no. For an internal assistant or searching your documents, a model of 20 to 30 billion parameters fits on a single card, for a much lower budget. A model of this size is justified for agents that code or drive tools, when quality takes precedence over cost.
| Machine | GPU memory | Price excl. tax |
|---|---|---|
| CoreIA RTX 5090 | 32 GB | €8,576 |
| CoreIA Radeon AI PRO R9700 | 32 GB | €3,469 |
Which model for your use?
Tell us what you want to run. We will tell you which machine, and if a smaller model is enough. Free quote, no commitment.
Frequently Asked Questions
What GPU memory is needed for DeepSeek-V4.1-Flash-1017?
The published repository weighs 475.7 GB, so you need about 480 GB with the cache. If you convert it to 4 bits, our estimate goes from 316 to 435 GB depending on its true size, which the sheet does not specify.
Can it be run on a single graphics card?
No. Even the RTX PRO 6000, with 96 GB, is very far from the count. You need an 8-card server, like our EPYC 8 GPU AI Server with optional cards.
What is its exact size?
The sheet for this version does not give it. The one for DeepSeek-V4.1-Flash announces 552 billion parameters for the model body; the repository counts about 763 billion in total. This is why we give ranges.
Can it be used in a company?
Yes. The license is MIT: commercial use allowed, while retaining the copyright notice. Read the full text of the license.
What speed can be expected?
We have not measured this model on our machines and therefore do not announce any speed.
What context does it accept?
DeepSeek announces up to one million tokens. A context this long requires more memory than the 32,768 tokens of our calculations.
Sources: DeepSeek, DeepSeek-V4.1-Flash-1017 sheet; DeepSeek, DeepSeek-V4.1-Flash sheet. Retrieved November 1, 2026: the memory figures are estimates from Radiance Systems.
