Text model · GLM
GL GLM-5.3-Flash: RAM and VRAM requirements
GLM-5.3-Flash needs about 204.8 GB to run at Q4_K_M (the Q4_K_M GGUF file is ~199.71 GB to download; KV cache and overhead add the rest), or about 322.7 GB at Q8_0. The lightest hardware that runs it is a high-memory machine.
GLM family · 320B params (Mixture-of-Experts: activates only 18B of 320B params per token, so generation is faster than the total size suggests) · released Aug 2026.
Shopping for hardware? See what runs GLM-5.3-Flash →
Q4_K_M and Q8_0 are GGUF download (file) sizes. Memory @ Q4 includes the KV cache and overhead, so it runs larger than the file on disk.
Will it run on your device?
GLM-5.3-Flash runs on 0 of 43 tracked devices at Q4_K_M.
Memory breakdown
How context length changes it
Longer context grows the KV cache, which for GLM-5.3-Flash is sized by its 18B active params, not the full 320B. It needs ~204.8 GB at 4k and ~337.9 GB at 128k.
Quantization sizes
| Quantization | Size on disk |
|---|---|
| Q2_K | 134 GB est |
| Q3_K_M | 156.4 GB est |
| Q4_K_M (default) | 199.71 GB |
| Q5_K_M | 228 GB est |
| Q6_K | 262.4 GB est |
| Q8_0 | 317.56 GB |
| FP16 | 642.81 GB |
Lower quant = smaller and faster, slightly lower quality. Q4_K_M is the common default.
Run it
llama-cli -hf unsloth/GLM-5.3-Flash-GGUF:Q4_K_M lms get unsloth/GLM-5.3-Flash-GGUF Which devices can run GLM-5.3-Flash?
NVIDIA GPUs
- Nvidia GeForce RTX 2060 (6GB) No
- Nvidia GeForce RTX 3060 Ti (8GB) No
- Nvidia GeForce GTX 1070 (8GB) No
- Nvidia GeForce RTX 3060 (12GB) No
- Nvidia GeForce RTX 4070 (12GB) No
- Nvidia GeForce RTX 4060 Ti (16GB) No
- Nvidia GeForce RTX 4080 (16GB) No
- Nvidia GeForce RTX 4090 (24GB) No
- Nvidia GeForce RTX 3090 (24GB) No
- Nvidia GeForce RTX 5090 (32GB) No
Apple Silicon Macs
- Apple M1 (8GB) No
- Apple M2 (16GB) No
- Apple M4 (16GB) No
- Apple M5 (16GB) No
- Apple M3 Pro (18GB) No
- Apple M4 (24GB) No
- Apple M4 Pro (24GB) No
- Apple M5 (32GB) No
- Apple M4 Pro (48GB) No
- Apple M5 Pro (48GB) No
- Apple M4 Max (64GB) No
- Apple M4 Max (128GB) No
- Apple M5 Max (128GB) No
- Apple M3 Ultra (256GB) No
RAM-only laptops
iPhone & iPad
Android
Same job, different size
Pick by what fits your memory: step down to free up VRAM, or step up if you have headroom.
Similar models
Head-to-head
FAQ
How much VRAM or RAM does GLM-5.3-Flash need?
At Q4_K_M, GLM-5.3-Flash needs about 204.8 GB (weights ~199.71 GB + KV cache + overhead) at a 4k context. At Q8_0 budget ~322.7 GB.
What is the Q4_K_M GGUF file size of GLM-5.3-Flash?
The Q4_K_M GGUF file is about 199.71 GB to download, and the Q8_0 GGUF is about 317.56 GB. That is the weights file on disk; to run it you also need room for the KV cache and overhead, so budget ~204.8 GB of memory at Q4_K_M.
Can GLM-5.3-Flash run on a laptop?
GLM-5.3-Flash is large; you need a high-memory Mac or multi-GPU setup at Q4_K_M.
Is GLM-5.3-Flash cheaper to run because it is a MoE model?
It is faster, not lighter. GLM-5.3-Flash activates only 18B of 320B params per token (so it runs quickly), but all experts must stay in memory, so it still needs memory for the full 320B.
Can I use GLM-5.3-Flash commercially?
Yes. GLM-5.3-Flash is licensed MIT, which permits commercial use.
Understand the numbers
Short guides to the ideas behind GLM-5.3-Flash's memory and quant figures.
Z.ai's fast tier of the 5.3 generation (the full GLM-5.3 has no public weights): MoE 320B total / 18B active (288 routed experts, 8 per token), 1M context, MIT. Q4 figure is unsloth's UD-Q4_K_XL split sum; even the 2-bit UD-Q2_K_XL is 108.72GB, so a 128GB unified-memory machine is the local entry point. Ollama lists it cloud-only.
Sources
Last validated 2026-09-07. Memory figures are estimates. See methodology.