How much VRAM does GLM-5.1 (MoE) need?

GLM-5.1 (MoE) requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run GLM-5.1 (MoE) on a consumer GPU?

GLM-5.1 (MoE) is a 754B parameter model and requires datacenter or multi-GPU setups. At Q4_K_M, it needs approximately 0.0 GB VRAM, which exceeds consumer GPU limits. It can run on H100 (80 GB) or A100 (80 GB) datacenter GPUs, or across multiple consumer GPUs with tensor parallelism.

What is GLM-5.1 (MoE)'s context window?

GLM-5.1 (MoE) supports a maximum context window of 131,072 tokens. The default context is 4,096 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for GLM-5.1 (MoE)?

For GLM-5.1 (MoE), start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does GLM-5.1 (MoE) compare to similar models?

GLM-5.1 (MoE) is a 754.0B MoE model from the GLM family. Browse other GLM models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

GLMMoEMIT

GLM-5.1 (MoE)

754.0B

Parameters

32.0B

Active

128K

Max Context

MoE

Architecture

Apr 7, 2026

Released

Text

Modality

About GLM-5.1 (MoE)

GLM-5.1 is Z.ai's flagship model for long-horizon agentic coding tasks. Built on a novel GlmMoeDSA architecture with 754B total parameters (256 routed + 1 shared experts, 8+1 active per token) across 78 layers, it combines Gated DeltaNet linear attention with standard attention and sparse MoE feed-forward networks — enabling efficient inference while delivering top-tier intelligence. Achieves state-of-the-art 58.4% on SWE-Bench Pro, 63.5% on Terminal-Bench 2.0, 95.3% on AIME 2026, and 86.2% on GPQA-Diamond. Uniquely designed for 8-hour sustained autonomous execution — breaking complex engineering tasks into iterative experiment-analyze-optimize loops. Supports 200K context window and 128K max output tokens. Available via API as glm-5.1 on Z.ai and BigModel.cn. Released April 7, 2026 under MIT license.

Agentic CodingSWE-benchLong-Horizon TasksEngineeringResearch

Technical Specifications

Total Parameters754.0B

Active Parameters32.0B per token

ArchitectureMixture of Experts

Total Experts257

Active Experts9 per token

Attention TypeHybrid Gated DeltaNet + Standard Attention (MoE FFN)

Hidden Dimensiond = 8,192

Transformer Layers80

Attention Heads64

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	128K ctx
Q4_K_M0.50 B/W ~97% of FP16	390.0Cluster / Multi-GPU	429.7Cluster / Multi-GPU
Q8_01.00 B/W ~100% of FP16	779.8Cluster / Multi-GPU	819.5Cluster / Multi-GPU
F162.00 B/W Reference	1559.2Cluster / Multi-GPU	1598.9Cluster / Multi-GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Other GLM Models

View All

GLM-4.5 (MoE)

Params

355.0B

Layers

Context

200K

32.0B active

Find the right GPU for GLM-5.1 (MoE)

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

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