EDGE OSDEEPSEEK · 1.6T PARAMETERS
DeepSeek-V4-Pro hardware and VRAM requirements
DeepSeek’s 1.6T-parameter flagship with 49B active per token and a 1M-token context, the larger sibling of DeepSeek-V4-Flash. DeepSeek-V4-Pro is a mixture-of-experts model with 49B of its 1.6T parameters work on each token. At FP4 + FP8 it needs about 976 GB of GPU memory for one user, and about 977 GB to serve a team of 16 with 8K context each.
Total parameters
1.6T
Active per token
49B
Context length
1M tokens
Released as
FP4 + FP8 (native)
Minimum VRAM
976 GB at FP4 + FP8
For a team of 16
977 GB at FP4 + FP8
VRAM needed to run DeepSeek-V4-Pro
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 |
|---|---|---|---|---|---|
| FP4 + FP8 (native) Released: FP4 expert weights, FP8 elsewhere (size estimated) | 928 GB | 976 GB | 977 GB | 980 GB | 1.24 TB |
Full fine-tuning in mixed precision needs about 25.76 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-Pro on-premise
For a team of 16 using DeepSeek-V4-Pro at FP4 + FP8 with 8K context each (about 977 GB), smallest fit first.
NVIDIA DGX B200
1.44 TB per system · 319 GB to spare.
NVIDIA H200 NVL
141 GB per GPU · Split across 8 GPUs in one server, 38 GB to spare.
AMD Instinct MI300X
192 GB per GPU · Split across 8 GPUs in one server, 405 GB to spare.
AMD Instinct MI325X
256 GB per GPU · Split across 8 GPUs in one server, 866 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.
NVIDIA H100 NVL
94 GB per GPU · Needs more than one 8-GPU server.
NVIDIA RTX PRO 6000 Blackwell
96 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-Pro for your team?
We will size the GPU server for your users and data, and set up DeepSeek-V4-Pro on it inside your own building. Free, no obligation.
DeepSeek-V4-Pro at a glance
Compare DeepSeek-V4-Pro with other models
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-Pro GPU questions
Is DeepSeek V4 Pro open source?
Yes. DeepSeek-V4-Pro’s weights are released under the MIT license, so you can download, self-host and use it commercially.
Does DeepSeek V4 Pro have vision?
No. DeepSeek-V4-Pro takes text input and produces text output, according to its NVIDIA listing.
How big is DeepSeek V4 Pro?
DeepSeek-V4-Pro has 1.6T parameters in total, of which 49B work on each token. The weights take about 928 GB at FP4 + FP8.
Can you run DeepSeek V4 Pro locally?
Yes, on data-center GPUs. At FP4 + FP8 it needs about 976 GB of GPU memory for one user, more than consumer graphics cards or laptops hold. A team of 16 fits on NVIDIA DGX B200.
How much VRAM to run DeepSeek V4 Pro?
DeepSeek-V4-Pro needs about 976 GB of GPU memory at FP4 + FP8 for one user with 8K context: 928 GB for the weights plus the KV cache and runtime overhead. Serving 16 people at once with 8K context each needs about 977 GB.
How many GPUs does DeepSeek V4 Pro need?
For 16 users with 8K context at FP4 + FP8 it needs about 977 GB. That fits on NVIDIA DGX B200.
Why does DeepSeek V4 Pro need so much memory if only 49B parameters are active?
It is a mixture-of-experts model. Only 49B parameters work on each token, which keeps it fast, but a different set of experts is picked for every token, so all 1.6T parameters must stay loaded in GPU memory.
Can I fine-tune DeepSeek V4 Pro?
LoRA fine-tuning at FP4 + FP8 needs roughly 1.24 TB with 4K sequences; it trains small adapter layers on a frozen copy of the model. Full fine-tuning needs about 25.76 TB, which calls for a multi-GPU training cluster.
How are these numbers calculated?
Weights are parameters × bytes per parameter. The KV cache uses 7.6 KB 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-Pro and NVIDIA: DeepSeek-V4-Pro 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-Pro 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.