EDGE OSDEEPSEEK · 552B PARAMETERS
DeepSeek-V4.1-Flash hardware and VRAM requirements
DeepSeek’s 552B mixture-of-experts model with only 8B active parameters for prefill and 16B for decoding, and a tiny per-token KV cache. DeepSeek-V4.1-Flash is a mixture-of-experts model with 16B of its 552B parameters work on each token. At FP8 it needs about 581 GB of GPU memory for one user, and about 582 GB to serve a team of 16 with 8K context each.
Total parameters
552B
Active per token
16B
Context length
1M tokens
Released as
FP8
Minimum VRAM
320 GB at 4-bit
For a team of 16
582 GB at FP8
VRAM needed to run DeepSeek-V4.1-Flash
GPU memory by precision, number of concurrent users and context length. Each user needs their own KV cache, so memory grows with users and context.
| Precision | Weights only | 1 user, 8K | 16 users, 8K | 16 users, 32K | LoRA fine-tune |
|---|---|---|---|---|---|
| FP8 Assumed: weight format not stated | 552 GB | 581 GB | 582 GB | 582 GB | 670 GB |
| 4-bit Estimated | 304 GB | 320 GB | 321 GB | 321 GB | 409 GB |
| BF16 Upcast | 1.1 TB | 1.16 TB | 1.16 TB | 1.16 TB | 1.25 TB |
Full fine-tuning in mixed precision needs about 8.89 TB: weights, gradients and optimizer states at roughly 16 bytes per parameter. LoRA trains small adapter layers on a frozen copy of the model instead, so it needs far less.
GPUs that can run DeepSeek-V4.1-Flash on-premise
For a team of 16 using DeepSeek-V4.1-Flash at FP8 with 8K context each (about 582 GB), smallest fit first.
NVIDIA DGX B200
1.44 TB per system · 714 GB to spare.
NVIDIA H100 NVL
94 GB per GPU · Split across 8 GPUs in one server, 95 GB to spare.
NVIDIA RTX PRO 6000 Blackwell
96 GB per GPU · Split across 8 GPUs in one server, 110 GB to spare.
AMD Instinct MI300X
192 GB per GPU · Split across 4 GPUs in one server, 110 GB to spare.
AMD Instinct MI325X
256 GB per GPU · Split across 4 GPUs in one server, 340 GB to spare.
NVIDIA H200 NVL
141 GB per GPU · Split across 8 GPUs in one server, 434 GB to spare.
NVIDIA DGX Spark
128 GB per system · Needs more than 2 linked systems.
NVIDIA L40S
48 GB per GPU · Needs more than one 8-GPU server.
Some of these setups need several GPUs working as one. Buying the GPUs is the easy part. Vanik Edge OS splits the model across them, keeps it running if a server fails and manages it all from one console. See how Edge OS clusters GPU servers
Weighing two of these? Compare AI hardware side by side, for example H100 vs H200, RTX PRO 6000 vs RTX 5090 or DGX Spark vs Mac Studio.
Running DeepSeek-V4.1-Flash for your team?
We will size the GPU server for your users and data, and set up DeepSeek-V4.1-Flash on it inside your own building. Free, no obligation.
DeepSeek-V4.1-Flash at a glance
FROM SIZING TO RUNNING
Sizing the hardware is the easy part.
Getting several GPUs to work as one system for a whole company is where most projects slow down. That is the part Vanik Edge OS does for you.
STEP 01 · THIS TOOL
Size it
Work out the GPU memory your models need and which GPUs or servers can hold them.
STEP 02 · VANIK EDGE OS
Cluster it
Edge OS joins your GPU servers into one cluster, splits a large model across them and keeps it running if a server fails. NVIDIA and AMD servers work side by side.
STEP 03 · VANIK EDGE OS
Run it
Your team gets AI apps with single sign-on and an audit trail, managed from one console, with your data staying inside your building.
DeepSeek-V4.1-Flash GPU questions
Can I run DeepSeek V4 Flash locally?
Yes, on data-center GPUs. At 4-bit it needs about 320 GB of GPU memory for one user, more than consumer graphics cards or laptops hold. A team of 16 fits on NVIDIA DGX B200.
What are the DeepSeek V4 Flash hardware requirements?
To run DeepSeek-V4.1-Flash locally you need about 320 GB of GPU memory at 4-bit for one user (582 GB for a team of 16), system RAM of at least 304 GB so the weights can be loaded, around 304 GB of fast NVMe storage for the model files, and an inference engine such as vLLM or SGLang. A team of 16 fits on NVIDIA DGX B200.
Is DeepSeek V4 Flash open source?
Yes. DeepSeek released DeepSeek-V4.1-Flash on Hugging Face on September 10, 2026 under the MIT license, so you can download, self-host and use it commercially.
How much VRAM does DeepSeek V4 Flash need?
DeepSeek-V4.1-Flash needs about 581 GB of GPU memory at FP8 for one user with 8K context: 552 GB for the weights plus the KV cache and runtime overhead. Serving 16 people at once with 8K context each needs about 582 GB.
How big is DeepSeek V4 Flash?
DeepSeek-V4.1-Flash has 552B parameters in total, of which 16B work on each token. The weights take about 304 GB at 4-bit.
How many GPUs does DeepSeek V4 Flash need?
For 16 users with 8K context at FP8 it needs about 582 GB. That fits on NVIDIA DGX B200.
Why does DeepSeek V4 Flash need so much memory if only 16B parameters are active?
It is a mixture-of-experts model. Only 16B parameters work on each token, which keeps it fast, but a different set of experts is picked for every token, so all 552B parameters must stay loaded in GPU memory.
Can I fine-tune DeepSeek V4 Flash?
LoRA fine-tuning at FP8 needs roughly 670 GB with 4K sequences; it trains small adapter layers on a frozen copy of the model. Full fine-tuning needs about 8.89 TB, which calls for a multi-GPU training cluster.
How are these numbers calculated?
Weights are parameters × bytes per parameter. The KV cache uses 890 bytes per token per user. We add about 5% of the weights plus 1 GB for runtime overhead and keep 10% of each GPU free. Figures are planning estimates; real usage depends on the inference engine and settings.
VRAM for similar models
Specifications from Hugging Face: deepseek-ai/DeepSeek-V4.1-Flash and NVIDIA: DeepSeek-V4.1-Flash model card. Memory and bandwidth are manufacturer figures; GPU counts are rounded to 1, 2, 4 or 8 per server. All figures are planning estimates. Last checked November 10, 2026.
Run DeepSeek-V4.1-Flash inside your own building
Vanik Edge OS runs open-weight models on NVIDIA DGX, AMD Instinct and other GPU servers on your premises, so your data never leaves.