GLM-5.2 – How to Run Locally

Hardware Requirements & Feasibility

• GLM‑5.2’s full model needs on the order of 750 GB–1.5 TB (FP8–FP16); “local” here usually means high‑end workstations or servers, not typical desktops or laptops.
• Realistic setups discussed: 512 GB DDR4/DDR5 + dual RTX 3090/RTX 6000 or similar; Mac Studio with 256–512 GB unified RAM; DGX Spark–class boxes; upcoming high‑RAM APUs (Strix/Medusa Halo) or Intel/Crescent Island–style accelerators.
• Many argue 256 GB RAM is a theoretical minimum; 512 GB+ is “realistic” for usable speeds and less extreme quantization.

Performance, TPS, and Memory Bandwidth

• Decode speed is repeatedly framed as “memory‑bandwidth math”:
  TPS ≈ active weights (GB) ÷ memory bandwidth (GB/s).
• With 40B active parameters at 4‑bit (20 GB) and ~100 GB/s bandwidth, ~5 tok/s is expected; MoE routing and speculation (MTP) can improve or hurt depending on bottlenecks.
• Reported local speeds:
  • GLM‑5.2 Q4 on mixed CPU+2×3090: ~6 tok/s (can climb with faster RAM/CPUs).
  • CPU‑only Q6: ~1 tok/s; 16 parallel streams don’t raise per‑stream TPS.
• Heavy offloading to CPU or disk is described as “unusable” for interactive work, especially due to very slow prompt prefill.

Quantization vs Quality

• Thread emphasizes that benchmark claims like “4‑bit dynamic is essentially lossless” can be misleading; KL divergence ≠ real‑task quality.
• Several users say they must go 1–2 bits higher (Q5/Q6) than claimed “lossless” Q4 for long‑context, complex tasks.
• GLM‑5.2’s advertised near‑GPT‑5.x performance only applies at FP8/FP16; at FP4/Q4 loss is modest, but at very low bitwidth (FP2/Q1–Q2) quality reportedly degrades to below strong mid‑tier frontier models.

Local vs Cloud Economics

• For individuals, running GLM‑5.2 locally at high quality is often judged “not economic”: hardware $10k–$90k, plus hundreds of watts of power, versus relatively cheap API access.
• For teams or companies already spending thousands/month on tokens, a $15k–$80k on‑prem server can break even in 1–3 years, especially with shared use and predictable privacy/compliance.
• Electricity cost comparisons show local inference can be cost‑comparable per token, but only after substantial capex.

Privacy, Control, and Motivation

• Strong interest in local models for:
  • Data privacy and avoiding vendor logging/denials of service.
  • Regulatory and geopolitical insulation.
  • Freedom from rate limits, caps, and “enshittification” of cloud products.

Competition, Model Landscape & Future

• Many see GLM‑5.2 as part of a broader open‑weights wave (alongside DeepSeek, Qwen, etc.) eroding claims of proprietary “moats.”
• Consensus: near‑term “sweet spot” for consumers is still ~27–35B models (e.g., Qwen3.6‑27B) fully in VRAM; 750B‑class models remain niche, expensive, and often too slow locally.