01What it can do
- MIT (free for commercial use)
- Native 1M-token context
- 40B active MoE: best performance/inference ratio at very large scale
- Advanced multilingual zh / en
- —Total size 753B: requires multiple server GPUs (multi-card H100/H200)
- —No official Ollama tag — install via HuggingFace
- —Not suitable for consumer configurations
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for GLM 5.2 753B-A40B?
To run GLM 5.2 753B-A40B locally with Q4 quantization, you need about 437 GB of VRAM. An option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395) — this model exceeds this mini-PC's GPU capacity: choose a smaller model or suitable infrastructure.
Why this choice? Our complete guide on BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395) →
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Too large for your machine? The kit gives you the model that fits in your VRAM
- Lifetime online access
- PDF + files
- Lifetime updates
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.