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:

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!