EDGE OSOPEN-WEIGHT LLM COMPARISON
DeepSeek-V4-Pro vs GLM-5.2Specs, VRAM and the hardware to run them
Both are 1M-token mixture-of-experts models under the MIT license. DeepSeek-V4-Pro is bigger (1.6T parameters, 49B active) but ships with 4-bit expert weights; GLM-5.2 has 744B with 40B active and ships in FP8.
Parameters
1.6T vs 744B
Active per token
49B vs 40B
Context
1M vs 1M
Memory, 1 user
976 GB vs 455 GB
Which should you choose?
CHOOSE DEEPSEEK-V4-PRO IF
You want the larger model and are comfortable with DeepSeek’s FP4 and FP8 weight format.
CHOOSE GLM-5.2 IF
You want a well-supported FP8 or NVFP4 build for coding work and a model that fits comfortably on one 8-GPU H200 or B200 server.
Still deciding between DeepSeek-V4-Pro and GLM-5.2?
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-Pro vs GLM-5.2 specs
From each developer’s model card. Green marks the lighter figure where less is better for running cost.
| Specification | DeepSeek-V4-Pro | GLM-5.2 |
|---|---|---|
| Developer | DeepSeek | Z.ai |
| Total parameters | 1.6T | 744B |
| Active per token | 49B | 40B |
| Design | Mixture of experts | Mixture of experts |
| Experts | Mixture of experts (count not stated) | 256 routed experts + 1 shared, 8 active per token |
| Context window | 1M tokens | 1M tokens |
| Input and output | Text input, text output | Text input, text output |
| Layers | Not stated | 78 layers |
| Attention | Hybrid Compressed Sparse Attention and Heavily Compressed Attention | DeepSeek Sparse Attention |
| License | MIT | MIT |
| Released weight formats | FP4 + FP8 (native) | FP8, NVFP4 (4-bit), BF16 |
| Weights at FP4 + FP8 / NVFP4 | About 928 GB | About 432 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 / NVFP4) | DeepSeek-V4-Pro | GLM-5.2 |
|---|---|---|
| 1 user, 8K context | About 976 GB | About 455 GB |
| 1 user, 128K context | About 976 GB | About 467 GB |
| 16 users, 8K context each | About 977 GB | About 467 GB |
| 64 users, 8K context each | About 983 GB | About 507 GB |
Which GPUs can run DeepSeek-V4-Pro and GLM-5.2
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-Pro: 1 user | DeepSeek-V4-Pro: 16 users | GLM-5.2: 1 user | GLM-5.2: 16 users |
|---|---|---|---|---|
| NVIDIA DGX Spark 128 GB per system | No | No | No | No |
| NVIDIA L40S 48 GB per GPU | No | No | No | No |
| NVIDIA RTX PRO 6000 Blackwell 96 GB per GPU | No | No | 8 GPUs | 8 GPUs |
| NVIDIA H100 NVL 94 GB per GPU | No | No | 8 GPUs | 8 GPUs |
| NVIDIA H200 NVL 141 GB per GPU | 8 GPUs | 8 GPUs | 4 GPUs | 4 GPUs |
| AMD Instinct MI300X 192 GB per GPU | 8 GPUs | 8 GPUs | 4 GPUs | 4 GPUs |
| AMD Instinct MI325X 256 GB per GPU | 8 GPUs | 8 GPUs | 2 GPUs | 4 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, GLM-5.2 should generate tokens about 1.2× faster for each user: it reads about 23 GB of weights per token (40B active at NVFP4) 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-Pro vs GLM-5.2 questions
What is the difference between DeepSeek-V4-Pro and GLM-5.2?
Both are 1M-token mixture-of-experts models under the MIT license. DeepSeek-V4-Pro is bigger (1.6T parameters, 49B active) but ships with 4-bit expert weights; GLM-5.2 has 744B with 40B active and ships in FP8.
DeepSeek-V4-Pro vs GLM-5.2: which is better to run on-premise?
Choose DeepSeek-V4-Pro if you want the larger model and are comfortable with DeepSeek’s FP4 and FP8 weight format. Choose GLM-5.2 if you want a well-supported FP8 or NVFP4 build for coding work and a model that fits comfortably on one 8-GPU H200 or B200 server.
Which needs less VRAM, DeepSeek-V4-Pro or GLM-5.2?
GLM-5.2. For one user with 8K context it needs about 455 GB, versus about 976 GB for DeepSeek-V4-Pro. For 16 users it needs about 467 GB versus 977 GB.
Which is faster, DeepSeek-V4-Pro or GLM-5.2?
On the same hardware, GLM-5.2 should generate tokens about 1.2× faster for each user: it reads about 23 GB of weights per token (40B active at NVFP4) 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-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 run GLM-5.2 locally?
Yes. At NVFP4 it needs about 455 GB of GPU memory for one user with 8K context, which fits on 2 × AMD Instinct MI325X. A team of 16 needs about 467 GB: 4 × NVIDIA H200 NVL.
Can I use DeepSeek-V4-Pro and GLM-5.2 commercially?
DeepSeek-V4-Pro is released under MIT. GLM-5.2 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-Pro; NVIDIA: DeepSeek-V4-Pro model card; Hugging Face: zai-org/GLM-5.2; NVIDIA NGC: GLM-5.2. Memory figures are planning estimates; real use depends on the inference engine and settings. Last checked November 12, 2026.