EDGE OSZ.AI · 320B PARAMETERS
GLM-5.3-Flash hardware and VRAM requirements
Z.ai’s 320B multimodal mixture-of-experts model with 18B active parameters and a 1M-token context. GLM-5.3-Flash is a mixture-of-experts model with 18B of its 320B parameters work on each token. At FP8 it needs about 337 GB of GPU memory for one user, and about 342 GB to serve a team of 16 with 8K context each.
Total parameters
320B
Active per token
18B
Context length
Up to 1M input tokens
Released as
FP8, NVFP4 (4-bit)
Minimum VRAM
192 GB at NVFP4
For a team of 16
342 GB at FP8
VRAM needed to run GLM-5.3-Flash
GPU memory by precision, number of concurrent users and context length. Each user needs their own KV cache, so memory grows with users and context.
| Precision | Weights only | 1 user, 8K | 16 users, 8K | 16 users, 32K | LoRA fine-tune |
|---|---|---|---|---|---|
| FP8 Released | 320 GB | 337 GB | 342 GB | 350 GB | 390 GB |
| NVFP4 (4-bit) 181 GB official NVFP4 build | 181 GB | 192 GB | 196 GB | 205 GB | 244 GB |
| BF16 Upcast | 640 GB | 673 GB | 678 GB | 686 GB | 726 GB |
Full fine-tuning in mixed precision needs about 5.15 TB: weights, gradients and optimizer states at roughly 16 bytes per parameter. LoRA trains small adapter layers on a frozen copy of the model instead, so it needs far less.
GPUs that can run GLM-5.3-Flash on-premise
For a team of 16 using GLM-5.3-Flash at FP8 with 8K context each (about 342 GB), smallest fit first.
NVIDIA DGX B200
1.44 TB per system · 954 GB to spare.
NVIDIA L40S
48 GB per GPU · Split across 8 GPUs in one server, 4.1 GB to spare.
NVIDIA RTX PRO 6000 Blackwell
96 GB per GPU · Split across 4 GPUs in one server, 4.1 GB to spare.
AMD Instinct MI300X
192 GB per GPU · Split across 2 GPUs in one server, 4.1 GB to spare.
AMD Instinct MI325X
256 GB per GPU · Split across 2 GPUs in one server, 119 GB to spare.
NVIDIA H200 NVL
141 GB per GPU · Split across 4 GPUs in one server, 166 GB to spare.
NVIDIA H100 NVL
94 GB per GPU · Split across 8 GPUs in one server, 335 GB to spare.
NVIDIA DGX Spark
128 GB per system · Needs more than 2 linked systems.
Some of these setups need several GPUs 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
Weighing two of these? Compare AI hardware side by side, for example H100 vs H200, RTX PRO 6000 vs RTX 5090 or DGX Spark vs Mac Studio.
Running GLM-5.3-Flash for your team?
We will size the GPU server for your users and data, and set up GLM-5.3-Flash on it inside your own building. Free, no obligation.
GLM-5.3-Flash at a glance
Compare GLM-5.3-Flash with other models
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-Flash GPU questions
How much VRAM to run GLM 5.3 Flash?
GLM-5.3-Flash needs about 337 GB of GPU memory at FP8 for one user with 8K context: 320 GB for the weights plus the KV cache and runtime overhead. Serving 16 people at once with 8K context each needs about 342 GB.
How big is GLM 5.3 Flash?
GLM-5.3-Flash has 320B parameters in total, of which 18B work on each token. The weights take about 181 GB at NVFP4.
Is GLM 5.3 Flash open source?
Yes. GLM-5.3-Flash’s weights are published under the MIT license, so you can download and self-host it.
Does GLM 5.3 Flash have vision?
Yes. GLM-5.3-Flash accepts text, images and video (mp4, mov, webm) as input and produces text, according to its NVIDIA listing.
Can I run GLM 5.3 Flash locally?
Yes, on data-center GPUs. At NVFP4 it needs about 192 GB of GPU memory for one user, more than consumer graphics cards or laptops hold. A team of 16 fits on NVIDIA DGX B200.
How many GPUs does GLM 5.3 Flash need?
For 16 users with 8K context at FP8 it needs about 342 GB. That fits on NVIDIA DGX B200.
Why does GLM 5.3 Flash need so much memory if only 18B parameters are active?
It is a mixture-of-experts model. Only 18B parameters work on each token, which keeps it fast, but a different set of experts is picked for every token, so all 320B parameters must stay loaded in GPU memory.
Can I fine-tune GLM 5.3 Flash?
LoRA fine-tuning at FP8 needs roughly 390 GB with 4K sequences; it trains small adapter layers on a frozen copy of the model. Full fine-tuning needs about 5.15 TB, which calls for a multi-GPU training cluster.
How are these numbers calculated?
Weights are parameters × bytes per parameter. The KV cache uses 22.0 KB per token per user. We add about 5% of the weights plus 1 GB for runtime overhead and keep 10% of each GPU free. Figures are planning estimates; real usage depends on the inference engine and settings.
VRAM for similar models
Specifications from Hugging Face: zai-org/GLM-5.3-Flash and NVIDIA NGC: GLM-5.3-Flash. Memory and bandwidth are manufacturer figures; GPU counts are rounded to 1, 2, 4 or 8 per server. All figures are planning estimates. Last checked November 10, 2026.
Run GLM-5.3-Flash inside your own building
Vanik Edge OS runs open-weight models on NVIDIA DGX, AMD Instinct and other GPU servers on your premises, so your data never leaves.