Can Desktop PC · GeForce RTX 4070 Ti SUPER · 32 GB RAM run GLM-5.3-Flash (320B-A18B)?
16 GB VRAM + 32 GB RAM · 672 GB/s memory bandwidth · GLM-5.3-Flash (320B-A18B) has 320B parameters (18B active per token).
Short answer
No. It doesn't fit in memory.
Needs ≈148 GB but only ≈39 GB of VRAM + system RAM is available after the OS and other apps.
Why it doesn’t fit
Even its smallest practical version (Q3_K_M) needs about 148 GB for the model, its context memory and the runtime. After Windows (≈4.5 GB), a browser with a few tabs (≈3.0 GB) and Open WebUI (≈1.0 GB), Desktop PC · GeForce RTX 4070 Ti SUPER · 32 GB RAM has about 39 GB left, so it’s 110 GB short.
Ways to make it work
Close everything else
Not enough on its own: with every other app closed it’s still about 107 GB short.
Pick a smaller model from the same family
Get more memory
You’d want at least 157 GB of VRAM plus RAM in total. The machines below are the cheapest that run it comfortably.
Check your exact setup
These pages assume typical settings. A shorter context or a lighter chat app can change the answer.
Check Desktop PC · GeForce RTX 4070 Ti SUPER · 32 GB RAM
The same applies to every workload we rate (chat, coding questions, coding in a repository, agentic coding and long documents): they all need at least as much memory as chat.
Other models for Desktop PC · GeForce RTX 4070 Ti SUPER · 32 GB RAM
The best-rated models for chat on this machine.
- gpt-oss-20b120–190 tok/sExcellent
- Gemma 4 26B-A4B130–220 tok/sExcellent
- MiniCPM5 2B120–200 tok/sExcellent
- Qwen3.5 9B40–65 tok/sExcellent
- Qwen3.5 4B75–120 tok/sExcellent
- Gemma 4 12B35–55 tok/sExcellent
Cheapest machines for GLM-5.3-Flash (320B-A18B)
Hardware rated comfortable or better for chat, lowest price first.
Different tool, context size or workload?
These pages use typical settings. The full check models your exact setup.