EDGE OSOPEN-WEIGHT LLM COMPARISON
DeepSeek-V4-Flash vs MiMo-V2.5Specs, VRAM and the hardware to run them
Two efficient 1M-token mixture-of-experts models under the MIT license. DeepSeek-V4-Flash has 284B parameters with 13B active and is text only; Xiaomi’s MiMo-V2.5 has 310B with 15B active and also understands images, audio and video.
Parameters
284B vs 310B
Active per token
13B vs 15B
Context
1M vs 1M
Memory, 1 user
177 GB vs 190 GB
Which should you choose?
CHOOSE DEEPSEEK-V4-FLASH IF
You only need text, want the smallest memory footprint (about 168 GB with its native FP4 experts), and value DeepSeek’s tooling support.
CHOOSE MIMO-V2.5 IF
You need one model that also handles images, audio and video, such as meeting recordings, scanned forms or site photos.
Still deciding between DeepSeek-V4-Flash and MiMo-V2.5?
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DeepSeek-V4-Flash vs MiMo-V2.5 specs
From each developer’s model card. Green marks the lighter figure where less is better for running cost.
| Specification | DeepSeek-V4-Flash | MiMo-V2.5 |
|---|---|---|
| Developer | DeepSeek | Xiaomi |
| Total parameters | 284B | 310B |
| Active per token | 13B | 15B |
| Design | Mixture of experts | Mixture of experts |
| Experts | Mixture of experts (count not stated) | Sparse mixture of experts |
| Context window | 1M tokens | 1M tokens |
| Input and output | Text input, text output | Text, image, audio and video input, text output |
| Layers | Not stated | Not stated |
| Attention | Hybrid Compressed Sparse Attention and Heavily Compressed Attention | Hybrid: sliding-window and global attention layers in a 6:1 ratio |
| License | MIT | MIT |
| Released weight formats | FP4 + FP8 (native) | FP8 (native), 4-bit |
| Weights at FP4 + FP8 / 4-bit | About 168 GB | About 180 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 / 4-bit) | DeepSeek-V4-Flash | MiMo-V2.5 |
|---|---|---|
| 1 user, 8K context | About 177 GB | About 190 GB |
| 1 user, 128K context | About 178 GB | About 194 GB |
| 16 users, 8K context each | About 178 GB | About 195 GB |
| 64 users, 8K context each | About 183 GB | About 211 GB |
Which GPUs can run DeepSeek-V4-Flash and MiMo-V2.5
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 | MiMo-V2.5: 1 user | MiMo-V2.5: 16 users |
|---|---|---|---|---|
| NVIDIA DGX Spark 128 GB per system | 2 systems | 2 systems | 2 systems | 2 systems |
| NVIDIA L40S 48 GB per GPU | 8 GPUs | 8 GPUs | 8 GPUs | 8 GPUs |
| NVIDIA RTX PRO 6000 Blackwell 96 GB per GPU | 4 GPUs | 4 GPUs | 4 GPUs | 4 GPUs |
| NVIDIA H100 NVL 94 GB per GPU | 4 GPUs | 4 GPUs | 4 GPUs | 4 GPUs |
| NVIDIA H200 NVL 141 GB per GPU | 2 GPUs | 2 GPUs | 2 GPUs | 2 GPUs |
| AMD Instinct MI300X 192 GB per GPU | 2 GPUs | 2 GPUs | 2 GPUs | 2 GPUs |
| AMD Instinct MI325X 256 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| 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 they should generate tokens at similar speeds: each reads about 7.7 GB to 8.7 GB of weights per token. Token generation is limited mostly by memory bandwidth. 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 MiMo-V2.5 questions
What is the difference between DeepSeek-V4-Flash and MiMo-V2.5?
Two efficient 1M-token mixture-of-experts models under the MIT license. DeepSeek-V4-Flash has 284B parameters with 13B active and is text only; Xiaomi’s MiMo-V2.5 has 310B with 15B active and also understands images, audio and video.
DeepSeek-V4-Flash vs MiMo-V2.5: which is better to run on-premise?
Choose DeepSeek-V4-Flash if you only need text, want the smallest memory footprint (about 168 GB with its native FP4 experts), and value DeepSeek’s tooling support. Choose MiMo-V2.5 if you need one model that also handles images, audio and video, such as meeting recordings, scanned forms or site photos.
Which needs less VRAM, DeepSeek-V4-Flash or MiMo-V2.5?
DeepSeek-V4-Flash. For one user with 8K context it needs about 177 GB, versus about 190 GB for MiMo-V2.5. For 16 users it needs about 178 GB versus 195 GB.
Which is faster, DeepSeek-V4-Flash or MiMo-V2.5?
On the same hardware they should generate tokens at similar speeds: each reads about 7.7 GB to 8.7 GB of weights per token. Token generation is limited mostly by memory bandwidth.
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 MiMo-V2.5 locally?
Yes. At 4-bit it needs about 190 GB of GPU memory for one user with 8K context, which fits on 2 × NVIDIA DGX Spark. A team of 16 needs about 195 GB: 2 × NVIDIA DGX Spark.
Can I use DeepSeek-V4-Flash and MiMo-V2.5 commercially?
DeepSeek-V4-Flash is released under MIT. MiMo-V2.5 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; Xiaomi: MiMo-V2.5; Hugging Face: XiaomiMiMo/MiMo-V2.5. Memory figures are planning estimates; real use depends on the inference engine and settings. Last checked November 12, 2026.