GLM-5.1
By Z.AI · China
Updated 2026-07-13
Overview
Z.AI's flagship MoE with 744B total and 40B active parameters under an MIT license. Ranked #1 open-weight model on Artificial Analysis as of April 2026.
When to pick this model
- Production agentic systems on dedicated server clusters
- Replacing closed frontier APIs with self-hosted weights
- Long-context document analysis up to 200K tokens
- Open-weight SWE-Bench-grade coding agents
- Commercial deployments that need MIT licensing
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 445 GB |
| Q5_K_M | 535 GB |
| Q8_0 | 800 GB |
| FP16 (no quantization) | 1488 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, GLM-5.1 is server-class even at Q4_K_M (445 GB). Stepping up to Q8_0 nearly doubles the footprint to 800 GB, and unquantized FP16 weights take 1488 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, GLM-5.1 needs roughly 512 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1 tokens/sec on entry-level GPUs, on the order of 5 tokens/sec on a mid-range card, and up to 15 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches GLM-5.1 to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for GLM-5.1 |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 445 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 445 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 445 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 445 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 445 GB at Q4_K_M |
Which GPU should you buy to run GLM-5.1?
To run GLM-5.1 locally at Q4, you need ~445 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| SWE-Bench Pro | 58.4 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- #1 open-weight model on Artificial Analysis (April 2026)
- 58.4 on SWE-Bench Pro, leading all open weights
- 200K context for whole-repo reasoning
- True MIT license with full commercial rights
Limitations
- 445GB+ in Q4 quantization requires a multi-GPU server
- No official Ollama tag at launch
- Operational complexity rules out single-workstation use
Typical workloads
In our catalog grid, GLM-5.1 is filed under Open Frontier, Reasoning, Multilingual — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks; multilingual workloads.
The 195k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: MoE · 744B/40B active · 200k ctx · Reasoning variant
Training: Successor to GLM-5 (February 2026).
The strongest open-weight model available today, provided you have the hardware to run a 744B MoE.
Quick start
# HuggingFace (GGUF) : unsloth/GLM-5.1-GGUFOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does GLM-5.1 need?
At the recommended Q4_K_M quantization, GLM-5.1 needs about 445 GB of VRAM. Q8_0 takes 800 GB, and unquantized FP16 weights take 1488 GB.
Can GLM-5.1 run without a GPU?
Yes — with roughly 512 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does GLM-5.1 support?
GLM-5.1 supports a 195k-token context window (200,000 tokens).
Can I use GLM-5.1 commercially?
Yes. GLM-5.1 is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is GLM-5.1 on consumer hardware?
Our compatibility engine estimates on the order of 5 tokens/sec on a mid-range GPU and up to 15 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of GLM-5.1 should I download first?
Start with Q4_K_M (445 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.