EDGE OSOPEN-WEIGHT LLM COMPARISON
DeepSeek-V4-Flash vs DeepSeek-V4-ProSpecs, VRAM and the hardware to run them
DeepSeek-V4-Flash and V4-Pro share the same 1M-token design and MIT license. Pro is the flagship with 1.6T parameters and 49B active; Flash has 284B with 13B active and needs about a fifth of the memory.
Parameters
284B vs 1.6T
Active per token
13B vs 49B
Context
1M vs 1M
Memory, 1 user
177 GB vs 976 GB
Which should you choose?
CHOOSE DEEPSEEK-V4-FLASH IF
You want DeepSeek’s V4 design on modest hardware: it fits on one 256 GB AMD MI325X or two H200s, and generates tokens much faster.
CHOOSE DEEPSEEK-V4-PRO IF
You want DeepSeek’s strongest open model and have a full 8-GPU B200-class server or larger.
Still deciding between DeepSeek-V4-Flash and DeepSeek-V4-Pro?
Tell us your models, users and data. We will recommend a setup and run it inside your own building with Vanik Edge OS. Free, no obligation.
DeepSeek-V4-Flash vs DeepSeek-V4-Pro specs
From each developer’s model card. Green marks the lighter figure where less is better for running cost.
| Specification | DeepSeek-V4-Flash | DeepSeek-V4-Pro |
|---|---|---|
| Developer | DeepSeek | DeepSeek |
| Total parameters | 284B | 1.6T |
| Active per token | 13B | 49B |
| Design | Mixture of experts | Mixture of experts |
| Experts | Mixture of experts (count not stated) | Mixture of experts (count not stated) |
| Context window | 1M tokens | 1M tokens |
| Input and output | Text input, text output | Text input, text output |
| Layers | Not stated | Not stated |
| Attention | Hybrid Compressed Sparse Attention and Heavily Compressed Attention | Hybrid Compressed Sparse Attention and Heavily Compressed Attention |
| License | MIT | MIT |
| Released weight formats | FP4 + FP8 (native) | FP4 + FP8 (native) |
| Weights at FP4 + FP8 / FP4 + FP8 | About 168 GB | About 928 GB |
VRAM needed to run them
GPU memory at the lowest released precision. Each user needs their own KV cache, so memory grows with users and context length.
| GPU memory needed (FP4 + FP8 / FP4 + FP8) | DeepSeek-V4-Flash | DeepSeek-V4-Pro |
|---|---|---|
| 1 user, 8K context | About 177 GB | About 976 GB |
| 1 user, 128K context | About 178 GB | About 976 GB |
| 16 users, 8K context each | About 178 GB | About 977 GB |
| 64 users, 8K context each | About 183 GB | About 983 GB |
Which GPUs can run DeepSeek-V4-Flash and DeepSeek-V4-Pro
Smallest setup on each system, with 8K context per user. Green fits on one GPU or system, amber needs several in one server, grey needs more than one 8-GPU server.
| Hardware | DeepSeek-V4-Flash: 1 user | DeepSeek-V4-Flash: 16 users | DeepSeek-V4-Pro: 1 user | DeepSeek-V4-Pro: 16 users |
|---|---|---|---|---|
| NVIDIA DGX Spark 128 GB per system | 2 systems | 2 systems | No | No |
| NVIDIA L40S 48 GB per GPU | 8 GPUs | 8 GPUs | No | No |
| NVIDIA RTX PRO 6000 Blackwell 96 GB per GPU | 4 GPUs | 4 GPUs | No | No |
| NVIDIA H100 NVL 94 GB per GPU | 4 GPUs | 4 GPUs | No | No |
| NVIDIA H200 NVL 141 GB per GPU | 2 GPUs | 2 GPUs | 8 GPUs | 8 GPUs |
| AMD Instinct MI300X 192 GB per GPU | 2 GPUs | 2 GPUs | 8 GPUs | 8 GPUs |
| AMD Instinct MI325X 256 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | 8 GPUs | 8 GPUs |
| NVIDIA DGX B200 1.44 TB per system | Yes, 1 system | Yes, 1 system | Yes, 1 system | Yes, 1 system |
Setups in amber or grey need several GPUs or servers 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
Which is faster?
On the same hardware, DeepSeek-V4-Flash should generate tokens about 3.7× faster for each user: it reads about 7.7 GB of weights per token (13B active at FP4 + FP8) versus 28 GB for DeepSeek-V4-Pro. Token generation is limited mostly by memory bandwidth, so fewer active bytes means faster replies. Real throughput also depends on batching, the inference engine and the GPU.
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-Flash vs DeepSeek-V4-Pro questions
What is the difference between DeepSeek-V4-Flash and DeepSeek-V4-Pro?
DeepSeek-V4-Flash and V4-Pro share the same 1M-token design and MIT license. Pro is the flagship with 1.6T parameters and 49B active; Flash has 284B with 13B active and needs about a fifth of the memory.
DeepSeek-V4-Flash vs DeepSeek-V4-Pro: which is better to run on-premise?
Choose DeepSeek-V4-Flash if you want DeepSeek’s V4 design on modest hardware: it fits on one 256 GB AMD MI325X or two H200s, and generates tokens much faster. Choose DeepSeek-V4-Pro if you want DeepSeek’s strongest open model and have a full 8-GPU B200-class server or larger.
Which needs less VRAM, DeepSeek-V4-Flash or DeepSeek-V4-Pro?
DeepSeek-V4-Flash. For one user with 8K context it needs about 177 GB, versus about 976 GB for DeepSeek-V4-Pro. For 16 users it needs about 178 GB versus 977 GB.
Which is faster, DeepSeek-V4-Flash or DeepSeek-V4-Pro?
On the same hardware, DeepSeek-V4-Flash should generate tokens about 3.7× faster for each user: it reads about 7.7 GB of weights per token (13B active at FP4 + FP8) versus 28 GB for DeepSeek-V4-Pro. Token generation is limited mostly by memory bandwidth, so fewer active bytes means faster replies.
Can I run DeepSeek-V4-Flash locally?
Yes. At FP4 + FP8 it needs about 177 GB of GPU memory for one user with 8K context, which fits on 2 × NVIDIA DGX Spark. A team of 16 needs about 178 GB: 2 × NVIDIA DGX Spark.
Can I run DeepSeek-V4-Pro locally?
Yes. At FP4 + FP8 it needs about 976 GB of GPU memory for one user with 8K context, which fits on 8 × NVIDIA H200 NVL. A team of 16 needs about 977 GB: 8 × NVIDIA H200 NVL.
Can I use DeepSeek-V4-Flash and DeepSeek-V4-Pro commercially?
DeepSeek-V4-Flash is released under MIT. DeepSeek-V4-Pro is released under MIT. Check each license before a commercial deployment.
How are these numbers calculated?
Memory figures come from our VRAM calculator method: weights at the lowest released precision, plus the KV cache for each user’s context, plus about 5 to 10% runtime overhead, keeping 10% of each GPU free. Where a developer does not publish the cache size we estimate it from the architecture. These are planning estimates, not benchmarks.
Related comparisons
Sources: Hugging Face: deepseek-ai/DeepSeek-V4-Flash-0731; Hugging Face blog: DeepSeek-V4; Hugging Face: deepseek-ai/DeepSeek-V4-Pro; NVIDIA: DeepSeek-V4-Pro model card. Memory figures are planning estimates; real use depends on the inference engine and settings. Last checked November 12, 2026.