EDGE OSOPEN-WEIGHT LLM COMPARISON
GLM-5.3 vs GLM-5.3-FlashSpecs, VRAM and the hardware to run them
GLM-5.3-Flash is Z.ai’s smaller sibling: 320B parameters with 18B active, versus 753B with 40B active for GLM-5.3. Flash adds image and video input and needs well under half the memory.
Parameters
753B vs 320B
Active per token
40B vs 18B
Context
1M vs Up to 1M input
Memory, 1 user
460 GB vs 192 GB
Which should you choose?
CHOOSE GLM-5.3 IF
You want Z.ai’s strongest model for coding and agent work and have a 4- to 8-GPU H200, B200 or MI325X-class server.
CHOOSE GLM-5.3-FLASH IF
You want lower cost and faster replies, need image or video input, or must fit on fewer GPUs, for example two H200s instead of four.
Still deciding between GLM-5.3 and GLM-5.3-Flash?
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-5.3 vs GLM-5.3-Flash specs
From each developer’s model card. Green marks the lighter figure where less is better for running cost.
| Specification | GLM-5.3 | GLM-5.3-Flash |
|---|---|---|
| Developer | Z.ai | Z.ai |
| Total parameters | 753B | 320B |
| Active per token | 40B | 18B |
| Design | Mixture of experts | Mixture of experts |
| Experts | 256 routed experts + 1 shared, 8 active per token | 288 experts, 8 active per token |
| Context window | 1M tokens | Up to 1M input tokens |
| Input and output | Text | Text, image and video input, text output |
| Layers | 78 layers (3 dense + 75 MoE) plus 1 MTP layer | 45 layers (reported by Z.ai) |
| Attention | DeepSeek Sparse Attention (indexer selects 2,048 tokens per query) | Alternating KDA linear-attention and sparse MLA layers, with IndexPool key compression and manifold-constrained hyper-connections |
| License | GLM-5.3 License (MIT-like) | MIT |
| Released weight formats | FP8 (native), NVFP4 (4-bit), BF16 | FP8, NVFP4 (4-bit), BF16 |
| Weights at NVFP4 / NVFP4 | About 437 GB | About 181 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 (NVFP4 / NVFP4) | GLM-5.3 | GLM-5.3-Flash |
|---|---|---|
| 1 user, 8K context | About 460 GB | About 192 GB |
| 1 user, 128K context | About 473 GB | About 195 GB |
| 16 users, 8K context each | About 473 GB | About 196 GB |
| 64 users, 8K context each | About 512 GB | About 210 GB |
Which GPUs can run GLM-5.3 and GLM-5.3-Flash
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-5.3: 1 user | GLM-5.3: 16 users | GLM-5.3-Flash: 1 user | GLM-5.3-Flash: 16 users |
|---|---|---|---|---|
| NVIDIA DGX Spark 128 GB per system | No | No | 2 systems | 2 systems |
| NVIDIA L40S 48 GB per GPU | No | No | 8 GPUs | 8 GPUs |
| NVIDIA RTX PRO 6000 Blackwell 96 GB per GPU | 8 GPUs | 8 GPUs | 4 GPUs | 4 GPUs |
| NVIDIA H100 NVL 94 GB per GPU | 8 GPUs | 8 GPUs | 4 GPUs | 4 GPUs |
| NVIDIA H200 NVL 141 GB per GPU | 4 GPUs | 4 GPUs | 2 GPUs | 2 GPUs |
| AMD Instinct MI300X 192 GB per GPU | 4 GPUs | 4 GPUs | 2 GPUs | 2 GPUs |
| AMD Instinct MI325X 256 GB per GPU | 2 GPUs | 4 GPUs | 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, GLM-5.3-Flash should generate tokens about 2.3× faster for each user: it reads about 10 GB of weights per token (18B active at NVFP4) versus 23 GB for GLM-5.3. 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-5.3 vs GLM-5.3-Flash questions
What is the difference between GLM-5.3 and GLM-5.3-Flash?
GLM-5.3-Flash is Z.ai’s smaller sibling: 320B parameters with 18B active, versus 753B with 40B active for GLM-5.3. Flash adds image and video input and needs well under half the memory.
GLM-5.3 vs GLM-5.3-Flash: which is better to run on-premise?
Choose GLM-5.3 if you want Z.ai’s strongest model for coding and agent work and have a 4- to 8-GPU H200, B200 or MI325X-class server. Choose GLM-5.3-Flash if you want lower cost and faster replies, need image or video input, or must fit on fewer GPUs, for example two H200s instead of four.
Which needs less VRAM, GLM-5.3 or GLM-5.3-Flash?
GLM-5.3-Flash. For one user with 8K context it needs about 192 GB, versus about 460 GB for GLM-5.3. For 16 users it needs about 196 GB versus 473 GB.
Which is faster, GLM-5.3 or GLM-5.3-Flash?
On the same hardware, GLM-5.3-Flash should generate tokens about 2.3× faster for each user: it reads about 10 GB of weights per token (18B active at NVFP4) versus 23 GB for GLM-5.3. Token generation is limited mostly by memory bandwidth, so fewer active bytes means faster replies.
Can I run GLM-5.3 locally?
Yes. At NVFP4 it needs about 460 GB of GPU memory for one user with 8K context, which fits on 2 × AMD Instinct MI325X. A team of 16 needs about 473 GB: 4 × NVIDIA H200 NVL.
Can I run GLM-5.3-Flash locally?
Yes. At NVFP4 it needs about 192 GB of GPU memory for one user with 8K context, which fits on 2 × NVIDIA DGX Spark. A team of 16 needs about 196 GB: 2 × NVIDIA DGX Spark.
Can I use GLM-5.3 and GLM-5.3-Flash commercially?
GLM-5.3 is released under GLM-5.3 License (MIT-like). GLM-5.3-Flash 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: zai-org/GLM-5.3; NVIDIA: GLM-5.3 model card; Hugging Face: zai-org/GLM-5.3-Flash; NVIDIA NGC: GLM-5.3-Flash. Memory figures are planning estimates; real use depends on the inference engine and settings. Last checked November 12, 2026.