GLM-5.3-Flash — lokalny model AI od Zhipu AI (Z.ai)
Autor: Jakub Rusinowski · Ostatnia aktualizacja:
The first natively multimodal model in the GLM-5 series, and the one Z.ai actually released under MIT — 320B total parameters with 18B active, text plus image and video in, a 1M-token context, and hybrid attention (linear attention for local dependencies, sparse attention for distant retrieval). Z.ai claims 63.4 on DeepSWE against GLM-5.2's 46.2. Note this is NOT the GLM-5.3 flagship: that is a separate 753B model under a bespoke licence with a hyperscaler security-review clause.
Warianty
Najmniejszy wariant GLM-5.3-Flash potrzebuje około 194 GB VRAM przy Q4_K_M — skwantyzowane wagi plus narzut frameworka, bez pamięci podręcznej KV.
| Model | VRAM przy Q4 | VRAM | Kontekst | Uruchom |
|---|---|---|---|---|
| GLM-5.3-Flash 320B-A18B → 320B (18B active) | ~194 GB | 1,048,576 | tag hostowany w chmurze |
Pamięć to skwantyzowane wagi plus narzut przy Q4_K_M, liczone tym samym silnikiem co sprawdzarka GPU i VRAM.
Jak uruchomić GLM-5.3-Flash lokalnie
Zainstaluj Ollama, a potem pobierz tag.
The Ollama tag `glm-5.3-flash:cloud` is cloud-hosted, so there is no local run command for this model.
Wybierz rozmiar powyżej, aby zobaczyć jego VRAM, szacowaną szybkość i polecenie instalacji.
Licencja
Commercial use permitted. No usage restrictions beyond attribution.
Dotyczy: GLM-5.3-Flash 320B-A18BPolecane GPU
Najtańsze GPU z naszego katalogu, które uruchomi GLM-5.3-Flash lokalnie (min. 194 GB VRAM), to Apple M3 Ultra (512 GB).
Czy uruchomię GLM-5.3-Flash na moim GPU?
- GLM-5.3-Flash na AMD Ryzen AI Max+ 395
- GLM-5.3-Flash na Apple M1 Ultra
- GLM-5.3-Flash na Apple M2 Max
- GLM-5.3-Flash na Apple M2 Ultra
- GLM-5.3-Flash na Apple M4 Max
- GLM-5.3-Flash na Apple M5 Max
- GLM-5.3-Flash na NVIDIA A100 80GB (PCIe)
- GLM-5.3-Flash na NVIDIA DGX Spark
- GLM-5.3-Flash na NVIDIA H100 80GB (PCIe)
- GLM-5.3-Flash na NVIDIA RTX PRO 6000 Blackwell
GLM-5.3-Flash — najczęściej zadawane pytania
How much VRAM does GLM-5.3-Flash need?
GLM-5.3-Flash needs about 194 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: GLM-5.3-Flash 320B-A18B (194 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run GLM-5.3-Flash on an RTX 4090 (24 GB)?
GLM-5.3-Flash's smallest variant needs about 194 GB, which exceeds a single RTX 4090 (24 GB). Use multiple GPUs, a higher-VRAM card, or Apple Silicon with large unified memory.
What quantization should I use for GLM-5.3-Flash?
Q4_K_M is the best balance of quality and VRAM for GLM-5.3-Flash in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
How do I run GLM-5.3-Flash with Ollama?
GLM-5.3-Flash has no local Ollama tag — the published tag is cloud-hosted, so running it sends your prompts to a hosted GPU rather than your own machine.