EDGE OSOPEN-WEIGHT LLM COMPARISON
GLM-5.2 vs Kimi K3Specs, VRAM and the hardware to run them
GLM-5.2 is Z.ai’s 744B coding model with 40B active; Kimi K3 is Moonshot AI’s 2.8T flagship with 104B active and image and video input. Both have 1M-token contexts.
Parameters
744B vs 2.8T
Active per token
40B vs 104B
Context
1M vs 1M
Memory, 1 user
455 GB vs 1.62 TB
Which should you choose?
CHOOSE GLM-5.2 IF
You want a capable coding model under the MIT license that fits on one 8-GPU server.
CHOOSE KIMI K3 IF
You need multimodal input or the largest open-weight model, and can run an 8-GPU AMD MI325X server or a multi-server NVIDIA cluster.
Still deciding between GLM-5.2 and Kimi K3?
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.
GLM-5.2 vs Kimi K3 specs
From each developer’s model card. Green marks the lighter figure where less is better for running cost.
| Specification | GLM-5.2 | Kimi K3 |
|---|---|---|
| Developer | Z.ai | Moonshot AI |
| Total parameters | 744B | 2.8T |
| Active per token | 40B | 104B |
| Design | Mixture of experts | Mixture of experts |
| Experts | 256 routed experts + 1 shared, 8 active per token | 896 experts + 2 shared, 16 active per token |
| Context window | 1M tokens | 1M tokens |
| Input and output | Text input, text output | Text, image and video input, text output |
| Layers | 78 layers | 93 layers |
| Attention | DeepSeek Sparse Attention | Hybrid: 69 Kimi Delta Attention (linear) layers and 24 Gated MLA layers |
| License | MIT | See the model card |
| Released weight formats | FP8, NVFP4 (4-bit), BF16 | MXFP4 (native) |
| Weights at NVFP4 / MXFP4 | About 432 GB | About 1.54 TB |
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 (NVFP4 / MXFP4) | GLM-5.2 | Kimi K3 |
|---|---|---|
| 1 user, 8K context | About 455 GB | About 1.62 TB |
| 1 user, 128K context | About 467 GB | About 1.62 TB |
| 16 users, 8K context each | About 467 GB | About 1.63 TB |
| 64 users, 8K context each | About 507 GB | About 1.65 TB |
Which GPUs can run GLM-5.2 and Kimi K3
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 | GLM-5.2: 1 user | GLM-5.2: 16 users | Kimi K3: 1 user | Kimi K3: 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 | 8 GPUs | 8 GPUs | No | No |
| NVIDIA H100 NVL 94 GB per GPU | 8 GPUs | 8 GPUs | No | No |
| NVIDIA H200 NVL 141 GB per GPU | 4 GPUs | 4 GPUs | No | No |
| AMD Instinct MI300X 192 GB per GPU | 4 GPUs | 4 GPUs | No | No |
| AMD Instinct MI325X 256 GB per GPU | 2 GPUs | 4 GPUs | 8 GPUs | 8 GPUs |
| NVIDIA DGX B200 1.44 TB per system | Yes, 1 system | Yes, 1 system | 2 systems | 2 systems |
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 2.5× faster for each user: it reads about 23 GB of weights per token (40B active at NVFP4) versus 57 GB for Kimi K3. 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.
GLM-5.2 vs Kimi K3 questions
What is the difference between GLM-5.2 and Kimi K3?
GLM-5.2 is Z.ai’s 744B coding model with 40B active; Kimi K3 is Moonshot AI’s 2.8T flagship with 104B active and image and video input. Both have 1M-token contexts.
GLM-5.2 vs Kimi K3: which is better to run on-premise?
Choose GLM-5.2 if you want a capable coding model under the MIT license that fits on one 8-GPU server. Choose Kimi K3 if you need multimodal input or the largest open-weight model, and can run an 8-GPU AMD MI325X server or a multi-server NVIDIA cluster.
Which needs less VRAM, GLM-5.2 or Kimi K3?
GLM-5.2. For one user with 8K context it needs about 455 GB, versus about 1.62 TB for Kimi K3. For 16 users it needs about 467 GB versus 1.63 TB.
Which is faster, GLM-5.2 or Kimi K3?
On the same hardware, GLM-5.2 should generate tokens about 2.5× faster for each user: it reads about 23 GB of weights per token (40B active at NVFP4) versus 57 GB for Kimi K3. Token generation is limited mostly by memory bandwidth, so fewer active bytes means faster replies.
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 run Kimi K3 locally?
Yes. At MXFP4 it needs about 1.62 TB of GPU memory for one user with 8K context, which fits on 8 × AMD Instinct MI325X. A team of 16 needs about 1.63 TB: 8 × AMD Instinct MI325X.
Can I use GLM-5.2 and Kimi K3 commercially?
GLM-5.2 is released under MIT. Kimi K3 is released under a license published on its model card. 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: zai-org/GLM-5.2; NVIDIA NGC: GLM-5.2; Hugging Face: moonshotai/Kimi-K3. Memory figures are planning estimates; real use depends on the inference engine and settings. Last checked November 12, 2026.