👁 Z.ai
Z.ai
GLM-5
Frontier
60.8KDownloads2.1KLikesFeb 2026Released200K tokensContextCustomLicense91 ExceptionalQuality
GLM-5 (744B parameters) requires approximately 475.9 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 548 GB of VRAM.
Get started
— copy & paste to run locallyCopy-paste commands to run GLM-5 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "zai-org/GLM-5" \
--hf-file "GLM-5-Q4_K_M.gguf" \
-c 4096 -ngl 99Quick specs
Parameters744B (40B active)
Architecturemoe (MoE)
Context200K tokens
Modalitytext
Min RAM290.2 GB
Rec. RAM453.8 GB (Q4_K_M)
LicenseCustom
FamilyGLM
✓ Code✓ Chat✓ Reasoning
About this model
- •Humanity’s Last Exam (HLE) & other reasoning tasks: We evaluate with a maximum generation length of 131,072 tokens (temperature=1.0, top_p=0.95,...
- •SWE-bench & SWE-bench Multilingual: We run the SWE-bench suite with OpenHands using a tailored instruction prompt. Settings: temperature=0.7,...
- •BrowserComp: Without context management, we retain details from the most recent 5 turns. With context management, we use the same discard-all...
- •Terminal-Bench 2.0 (Terminus 2): We evaluate with the Terminus framework using timeout=2h, temperature=0.7, top_p=1.0, max_new_tokens=8192, with a...
- •Terminal-Bench 2.0 (Claude Code): We evaluate in Claude Code 2.1.14 (think mode, default effort) with temperature=1.0, top_p=0.95,...
Related models
Your hardware
Detecting...
Quantization options
VRAM estimates by quant level
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 290.2 GB | Low | — |
Q3_K_S | 3 | 364.6 GB | Low | — |
NVFP4 | 4 | 416.6 GB | Medium | — |
Q4_K_M | 4 | 453.8 GB | Medium | — |
Q5_K_M | 5 | 535.7 GB | High | — |
Q6_K | 6 | 610.1 GB | High | — |
Q8_0 | 8 | 796.1 GB | Very High | — |
F16 | 16 | 1525.2 GB | Maximum | — |
Quality benchmarks
GLM-5 benchmark scores
Coding
SWE-bench Verified77.8%
HumanEval+—
Aider Polyglot—
LiveCodeBench—
Reasoning
MMLU-Pro70.4%
GPQA Diamond86.0%
MATH-500—
ARC Challenge—
Source: official · 2026-02-20
Hardware compatibility
Fit estimates across all hardware
Computing compatibility...
Memory breakdown
Reference: RTX 2060 6GB
Weights453.8 GB
KV Cache19.0 GB
Runtime2.4 GB
Headroom0.6 GB
Frequently asked questions
FAQ — GLM-5
See also