Can I run this LLM?
Can your computer run GLM-5.3 (MoE)?
GLM · Publisher model license
Memory & fit
Needs about 255.1 GB of memory at its smallest quantization (4K context). It runs on 5 of 88 tracked machines.
Planning a large-memory GLM-5.3 deployment
Content reviewed
Smallest listed pack · Q2_K
- Weights
- 253.9 GB
- KV cache
- 0.5 GB
- Runtime reserve
- 0.8 GB
- Total requirement
- 255.1 GB
Estimated at 4,096 tokens, llama.cpp, FP16 K/V cache, one stream and one GPU. Total = weights + cache + runtime reserve. Budget and reserve are site assumptions, not measured free memory. Actual peak allocation and runtime support can differ.
Sparse compute still needs weight storage
GLM-5.3 is a mixture-of-experts model aimed at complex coding and long-horizon work. Activating only part of its expert network for each token reduces the work per step; it does not mean that only the active experts need storage. Use the complete listed pack footprint shown here when assessing a large-memory machine or a multi-device deployment.
Publisher recipes describe server deployments
The official vLLM recipe requires version 0.28 or newer and shows eight H200/H20 GPUs for FP8, or eight B200 GPUs for full-context serving. These are examples for that runtime and weight format, not requirements measured from this GGUF estimate. The official configuration allows 1,048,576 tokens; the short-context calculation shown here does not validate that allocation.
Compare a practical execution path
Large local servers, pooled accelerators and host-memory offload are different deployment choices; inspect their capacity, backend and transfer costs. A single consumer GPU cannot be judged by the active-parameter headline. If the complete footprint exceeds your setup, compare a smaller model or hosted execution. Estimated fit does not establish a successful load, benchmark result or throughput guarantee.
Read the publisher license
GLM-5.3 has its own model license. Check the linked terms for your use, especially when providing a hosted model service.
Smaller models to compare
Same-context catalog estimates; speed is unmeasured and task quality can differ.
See the calculation methodology, or contribute a measured run with setup details.