GLM-5.2 UD-IQ2_M runs at ~7.3 tokens per second on 4×RTX 3090s with 192GB DDR5 RAM using llama.cpp expert offload. Reducing quantization from IQ2 to IQ1 provided no speed gain, while increasing CPU threads from 6 to 12 improved performance by 22%. Decode is limited by CPU compute, not memory bandwidth, and the offloaded experts must be explicitly distributed across GPUs to avoid out-of-memory errors.
GLM-5.2 (744B, 2-bit) achieves 7.3 tok/s on 4×3090 with 192GB RAM
GLM 5.2 Achieves 98% Max Intelligence with Less Than Half Tokens
GLM 5.2 demonstrates 98% of maximum intelligence in coding tasks using less than half of its total token budget, according to a technical report by z_ai. The model's reasoning efficiency has improved significantly, with token usage increasing from 16.7k to 36.7k between GLM 5.1 and GLM 5.2, though high-level settings may strain local hardware performance.
Running GLM-5.2 on CPU Only with Local Setup
A user runs GLM-5.2 locally on a Dell PowerEdge R740 with dual Xeon 6248R CPUs and 768GB RAM, using ik_llama.cpp for improved CPU inference. After isolating one NUMA node for optimal performance, they achieve 4–5.5 tokens per second in chat and about 3 tokens per second in coding tasks, noting the model shows 'frontier vibes' during code generation despite limited usability on this hardware.
GLM-5.2-FP8 HGX-H200 SGLang Docker Deployment Config
A user shares a Docker configuration for running GLM-5.2-FP8 on HGX-H200 hardware using SGLang. The setup achieves 262k context length and 70 tokens per second with 8 tensor parallelism, using a memory fraction of 0.83. The user notes that vLLM official recipes do not work on H200 due to KV cache FP8 quantization limitations on the DSV3 architecture.
GLM 5.2 on 4x Sparks: Reasonable?
A user asks whether running GLM-5.2 on four Ascend GX10 chips (DGX Sparks) is feasible. They inquire about 4-bit quantization using 512GB unified memory and estimate prompt and output token speeds for 100k context length, noting no existing performance data is available online.
GLM-5.2 Claims Top Position in Frontend Coding with Speculative Decoding
GLM-5.2, a 744B parameter model from Z.ai, has been evaluated as the top frontend coding model globally, outperforming all Opus versions including Opus 4.8. This achievement is highlighted in third-party evaluations that validate official offline tests, marking a significant milestone for a model of its size, particularly in the competitive frontend coding domain.