Z.ai
GLM-5.2
Frontera
231.2KDescargas3.5KMe gustaJun 2026Publicado200K tokensContextoMITLicencia93 ExcepcionalCalidad
GLM-5.2 (753.2999877929688B parameters) requires approximately 481.6 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 40B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 554 GB of VRAM.
Comenzar
— copia y pega para ejecutar en localCopy-paste commands to run GLM-5.2 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "zai-org/GLM-5.2" \
--hf-file "GLM-5.2-Q4_K_M.gguf" \
-c 4096 -ngl 99Quick specs
Parameters753.3B (40B active)
Architecturemoe (MoE)
Context200K tokens
Modalitytext
Min RAM293.8 GB
Rec. RAM459.5 GB (Q4_K_M)
LicenseMIT
FamilyGLM
✓ Code✓ Chat✓ Reasoning
About this model
- •Native 1M-token context for repository-scale and long-horizon agentic work.
- •Improved DSA MoE architecture (256 routed experts, 8 active per token, 1 shared) with MLA-style latent attention for reduced KV cost.
- •Flexible coding effort levels for balancing latency against solution quality.
- •Successor to GLM-5.1, tuned for sustained multi-round tool use.
Modelos relacionados
Opciones de cuantización
Estimaciones de VRAM por nivel de cuantización
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 293.8 GB | Low | — |
Q3_K_S | 3 | 369.1 GB | Low | — |
NVFP4 | 4 | 421.8 GB | Medium | — |
Q4_K_M | 4 | 459.5 GB | Medium | — |
Q5_K_M | 5 | 542.4 GB | High | — |
Q6_K | 6 | 617.7 GB | High | — |
Q8_0 | 8 | 806.0 GB | Very High | — |
F16 | 16 | 1544.3 GB | Maximum | — |
Compatibilidad de hardware
Estimaciones de encaje en todo el hardware
Computing compatibility...
Desglose de memoria
Reference: RTX 2060 6GB
Weights459.5 GB
KV Cache19.0 GB
Runtime2.4 GB
Headroom0.6 GB
Preguntas frecuentes
FAQ — GLM-5.2
Ver también