GLM 5.2 753B-A40B

When to pick this model

• Multi-GPU server deployments needing the largest available context (1M tokens)
• Bilingual zh/en chat and code workloads at flagship scale
• Teams needing MIT licensing without sacrificing context length
• Data-center inference where MoE efficiency (40B active) matters more than total footprint

VRAM requirements by quantization

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 5.2 753B-A40B is server-class even at Q4_K_M (437 GB). Stepping up to Q8_0 nearly doubles the footprint to 806 GB, and unquantized FP16 weights take 1506 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, GLM 5.2 753B-A40B needs roughly 979 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 1.5 tokens/sec on entry-level GPUs, on the order of 2.5 tokens/sec on a mid-range card, and up to 5 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches GLM 5.2 753B-A40B 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.

Which hardware should you buy to run GLM 5.2 753B-A40B?

To run GLM 5.2 753B-A40B locally at Q4, you need ~437 GB for Q4 weights alone. Hardware option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395). This model exceeds the practical GPU memory of this mini PC. Choose a smaller model or larger infrastructure.

Strengths

• MIT license (unrestricted commercial use)
• Native 1M-token context
• 40B active MoE gives the best performance-to-inference-cost ratio at this scale
• Advanced bilingual zh/en capability

Limitations

• 753B total size requires multiple server-class GPUs (multi-card H100/H200)
• No official Ollama tag — HuggingFace install only
• Not viable on consumer-grade hardware

Typical workloads

In our catalog grid, GLM 5.2 753B-A40B is filed under Multi-GPU Server, Bilingual zh/en, Long Context 1M — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multilingual workloads.

The 976k-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 · 753B total parameters / 40B active · 1M-token context

Training: GLM 5.2 (Zhipu / Z.ai): successor to GLM 5, MoE architecture with 40B active parameters, native 1M-token context. MIT license, optimized for chat / code / multilingual (zh ↔ en).

Quick start

Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.

Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.

# HuggingFace : zai-org/GLM-5.2

Or 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 5.2 753B-A40B need?

At the recommended Q4_K_M quantization, GLM 5.2 753B-A40B needs about 437 GB of VRAM. Q8_0 takes 806 GB, and unquantized FP16 weights take 1506 GB.

Can GLM 5.2 753B-A40B run without a GPU?

Yes — with roughly 979 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 5.2 753B-A40B support?

GLM 5.2 753B-A40B supports a 976k-token context window (1,000,000 tokens).

Can I use GLM 5.2 753B-A40B commercially?

Yes. GLM 5.2 753B-A40B is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is GLM 5.2 753B-A40B on consumer hardware?

Our compatibility engine estimates on the order of 2.5 tokens/sec on a mid-range GPU and up to 5 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of GLM 5.2 753B-A40B should I download first?

Start with Q4_K_M (437 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.