Models / Run locally
Run GLM-5.2 locally
Z.ai · ≈355B-class MoE (≈32B active) · open weights
≈210GB at 4-bit: multi-GPU server or high-memory Mac Studio. Z.ai's generous hosted free tier is the practical way to try it before committing hardware.
By quantization
Hardware requirements
| Quantization | ≈ File size | ≈ Memory needed | Runs on |
|---|---|---|---|
| Q4_K_M (4-bit) | 206 GB | 237 GB | Multi-GPU servers (4–8× 80GB) or 512GB Mac Studio clusters |
| Q8_0 (8-bit) | 380 GB | 437 GB | Multi-GPU servers (4–8× 80GB) or 512GB Mac Studio clusters |
| FP16 (full) | 746 GB | 858 GB | Multi-node GPU clusters — datacenter serving only |
Sizes are computed from the ≈355B parameter count with standard GGUF math and ~15% runtime overhead — verify against the model card before buying hardware. As a mixture-of-experts model only ≈32B parameters are active per token, which helps speed — but the full weights still have to fit in memory.
Local setup
How to run it
Grab a community GGUF conversion from Hugging Face and serve it with llama.cpp, LM Studio, or (for multi-GPU rigs) vLLM with the original weights. Pick the quantization from the table above that fits your memory.
For serious throughput on server hardware, vLLM with tensor parallelism is the standard serving stack; llama.cpp and MLX (Apple silicon) win below that line.
The API route
Or skip the GPU entirely
The same model is one API call away at $0.5 / M input · $1.8 / M output — no download, no VRAM math, metered to the exact call. Free starter credits cover your first runs, and because the weights are open, nothing locks you in: start metered, move to your own hardware if sustained volume ever justifies it.
FAQ
Frequently asked questions
Can my GPU run GLM-5.2?
GLM-5.2 is ≈355B-class MoE (≈32B active). At 4-bit quantization you need roughly 237GB of memory (multi-gpu servers (4–8× 80gb) or 512gb mac studio clusters). ≈210GB at 4-bit: multi-GPU server or high-memory Mac Studio. Z.ai's generous hosted free tier is the practical way to try it before committing hardware.
Is there a GLM-5.2 GGUF?
Open-weight releases in this family get community GGUF conversions on Hugging Face shortly after release — search the model name plus "GGUF" and pick the quantization your memory allows from the table above.
Is running GLM-5.2 locally cheaper than the API?
Only at sustained volume. The API price is $0.5 / M input · $1.8 / M output with no hardware, electricity, or ops cost — a machine that can serve this model costs more per month idle than most teams' entire inference bill. Prototype metered, self-host when utilization justifies it.
Can I use the outputs commercially?
Open weights; commercial use permitted per the Z.ai model license.
Explore
Run other models locally
One key. Every model. Exact prices.
Prototype on GLM-5.2 with free starter credits while the weights download. Free starter credits included — no subscription required.