GLM Z.ai Released 2026-02 reasoningthinkingagentic

GLM-5

Z.ai's flagship reasoning model. 744B total parameters, 40B active. Built for complex systems engineering and long-horizon agentic tasks. Frontier-class on SWE-Bench Pro.

Best for frontier-tier reasoning and agentic engineering
Sizes 744B (40B active MoE)
Context 128K
License MIT
Min VRAM (default size, Q4) 256 GB
Rec VRAM 512 GB

What is GLM-5?

GLM-5 is Z.ai's MoE open-weights language model. Released in 2026-02, it ships in 1 size (744B (40B active MoE)) and is licensed MIT. The most popular use case is frontier-tier reasoning and agentic engineering.

VRAM and hardware

The 744B (40B active MoE) size fits in 256 GB of VRAM.

Size Min VRAM (Q4_K_M) Recommended VRAM Tokens/sec on 3090
744B (40B active MoE) 256 GB 384 GB ~10 tok/s

How to run GLM-5 locally

Option 1: Ollama (simplest)

ollama pull glm-5
ollama run glm-5

Option 2: Mullama (production)

mullama pull glm-5
mullama run glm-5

Option 3: llama.cpp (CLI)

# Download a GGUF from Hugging Face (search "glm-5 gguf")
./llama-cli -m glm-5.Q4_K_M.gguf -p "Hello, AI!"

Option 4: Python with Mullama or llama-cpp-python

from mullama import Model, Context
model = Model.load("glm-5.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256))

What you can build with GLM-5

  • frontier reasoning
  • agentic coding
  • long-horizon tasks

Hardware it fits on

  • Apple Silicon (8GB): 744B (40B active MoE) in MLX
  • Apple Silicon (16GB): 744B (40B active MoE) in MLX
  • Apple Silicon (32GB): 744B (40B active MoE) in MLX or GGUF
  • Apple Silicon (64GB+): 744B (40B active MoE) in MLX
  • RTX 3090 (24GB): 744B (40B active MoE) at Q4_K_M
  • RTX 4090 (24GB): same as 3090, ~30% faster
  • RTX 5090 (32GB): 744B (40B active MoE) at Q8_0
  • 2× RTX 6000 Ada (96GB total): 744B (40B active MoE) at Q4_K_M

Related models in the GLM family

  • GLM-5.1 — 744B (40B active MoE) · next-generation frontier reasoning, agentic engineering
  • GLM-5.2 — 744B (40B active MoE) · latest Z.ai frontier reasoning model (2026 mid-year)
  • GLM-4.7 — mid-size MoE · advanced coding and agentic workflows
  • GLM-4.7 Flash — 30B · strong 30B coder that runs on a single 3090/4090

Other models you might consider

  • Llama 3.1 (Meta) — 8B, general-purpose chat, agentic workflows, code
  • Llama 3.2 (Meta) — 1B, low-end hardware, mobile, edge deployment
  • Llama 3.2 Vision (Meta) — 11B, vision + chat on desktop GPUs
  • Llama 3.3 (Meta) — 70B, best-in-class 70B for desktops
  • Llama 4 (Meta) — Scout 17B (109B MoE), vision + long context (10M tokens)
  • Qwen 2.5 (Alibaba) — 0.5B, strongest 7B-32B on consumer hardware