zai-org · glm4_moe_lite
GLM-4.7-Flash
GLM-4.7-Flash by zai-org: a 31.2B-parameter mixture-of-experts open-weight model under the mit license.
1,861,863 downloads · 1,844 likes · Model card · synced 2026-09-19
Specifications
- Parameters
- 31.2B (2B active)
- Architecture
- moe · glm4_moe_lite
- Layers
- 47
- Hidden size
- 2,048
- KV heads · head dim
- 20 · 102
- Vocabulary
- 154,880
- Native dtype
- —
- Context window
- 202,752 tokens
- Released
- 2026-01-19
- Last modified on Hub
- 2026-01-29
Features & licensing
- License
- mit
- Commercial use
- Yes
- Modalities
- text
- Tool / function calling
- No
- Reasoning mode
- No
- Languages
- EN, ZH
- Quantised variants on Hub
- AWQ, FP8, GGUF, bitsandbytes
- Pipeline
- text-generation
Task fit
Editorial scores (0–100) used by the recommendation engine.
- Chat70
- RAG / Q&A70
- Code70
- Summarisation70
- Extraction70
- Agents70
Hardware to run GLM-4.7-Flash
Weights need about 69 GB at FP16, 36 GB at INT8 and 21 GB at INT4. The KV cache adds roughly 384 MB per 1,000 tokens per request. For a reference workload of 5 requests per second with 1,500 input and 300 output tokens, total GPU memory is around 38 GB, which fits on 1 × NVIDIA A100 (80 GB).
| Configuration | VRAM | Utilisation | Est. first token | Cloud / month |
|---|---|---|---|---|
| 2 × NVIDIA L4 (24 GB) | 48 GB | 79 % | ~0.5 s | $1,168 |
| 1 × NVIDIA L40S (48 GB) | 48 GB | 79 % | ~0.2 s | $1,387 |
| 1 × NVIDIA A100 (80 GB)recommended | 80 GB | 47 % | ~0.1 s | $2,336 |
| 1 × NVIDIA H100 (80 GB) | 80 GB | 47 % | ~0.0 s | $3,285 |
Estimates only. Run an assessment for your own traffic.
Auto-imported from Hugging Face. Architecture, license and feature data are synced from the model repository; the task-fit scores below are provisional defaults until a curator reviews them.