---
title: "DeepSeek V4.1 Flash is built for datacenters"
date: 2026-09-10
canonical: https://solmaz.io/x/2097959978200076724/
x_url: https://x.com/onusoz/status/2097959978200076724
license: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
---

DeepSeek V4.1 Flash weights are out! ⚡️⚡️⚡️

I have good news and bad news

Don't be fooled by "V4".1, this is a different architecture

V4 was 284B total, 13B active
V4.1 has a 552B MoE backbone + 196B engram conditional-memory parameters with 16B active parameters

Bad news first. A single DGX Spark will likely not be able to hold all those parameters 😢 You will likely need 2 Sparks, or in general, a workstation with 256 GB. Could be a good time to get a loan... (not financial advice)

DeepSeek has never claimed they were building for local use, but with this architecture, they show us that their main priority is for very efficient use in datacenters, with HBM, not local

Good news:

Looking at active parameters, you might be bummed out that decode will be slower on this, at 13/16 ~ 81% of the speed of V4

But wait!!!

KV cache is 4x smaller. So decoding on this model will be a lot faster with concurrent sessions. And with speculative decoding, it seems like it might have 2-4x the throughput at scale, compared to V4. Back of the envelope calculation, I might pull back this prediction

The small KV cache will also do something good for local inference, but I need more time to calculate how much. Take these with a grain of salt. Give your agent my formulation, and let me know if it looks like I made an error somewhere: https://solmaz.io/llm-throughput-upper-bounds

Original safetensors: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash

*Part 1/2 of a thread; root: https://solmaz.io/x/2097959978200076724/*