Community Radar.

“GLM-5.2 is a win for local AI”

I know GLM 5.2's massive 753B footprint means none of us are running it at home without an enterprise cluster, but having a true frontier-level, MIT-licensed coding agent out in the wild makes me optimistic. The distillation potential here is massive. Once the community starts fine-tuning smaller 8B and 70B architectures on GLM 5.2's reasoning and synthetic datasets, our daily driver local setups are going to see huge improvements over the next few months. **Edit:** I did not expect so many people saying they can run it on local hardware. Here is the data spec: |**Quantization Level**|**Memory Required**|**Minimum Hardware Setup**| |:-|:-|:-| |**FP8 Weights**|744 GB to 890 GB|8x H200 (141GB) or 8x H100 (80GB) server node| |**4-bit (Q4\_K\_M)**|476 GB to 500 GB|Mac Studio cluster or 6x 80GB enterprise GPUs| |**2-bit (Q2\_K\_XL)**|241 GB to 280 GB|Single 256GB Mac Studio (Ultra) or RTX 4090 + 256GB system RAM| |**1-bit Dynamic**|176 GB to 180 GB|192GB Mac Studio or 24GB GPU + 192GB system RAM| # Model & Dataset Facts * **Pre-Training Data:** Trained on a corpus of 28.5 trillion tokens. * **Architecture Scale:** 753B total parameters, activating roughly 40B parameters per token during inference. * **Context Capacity:** Natively supports a 1,000,000-token context window and up to 131,072 output tokens per response. # KV Cache VRAM Scaling (Per 100k / 1M Tokens) Utilizing the 1M context window requires significant additional VRAM strictly for the KV cache. This scaling depends entirely on your cache quantization: * **16-bit (FP16/BF16):** Adds **15–20 GB per 100k tokens** (\~150–200 GB extra for the full 1M context). * **8-bit (FP8/INT8):** Adds **7.5–10 GB per 100k tokens** (\~75–100 GB extra for the full 1M context). This balances accuracy and memory. * **4-bit (INT4):** Adds **3.5–5 GB per 100k tokens** (\~35–50 GB extra for the full 1M context). Drastically lowers memory requirements but can degrade long-context retrieval accuracy. **NOTE:** I gathered this information online and these are estimates. For full transparency, I did use AI to generate the table and break the data down. I lack the editing patience to format this all myself...I am only human!