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.
GLM 5.2 Achieves 98% Max Intelligence with Less Than Half Tokens
CPU-only GLM 5.2: Epyc and 512GB RAM
A user tested the 4-bit version of GLM-5.2 (GLM-5.2-UD-Q4_K_XL) on a server equipped with an Epyc Rome 7452 processor and 512GB of RAM. The model was evaluated using a complex coding prompt requiring the creation of a self-contained 3D arena game in HTML, CSS, and JavaScript.
$1800 GPU cost runs Qwen3.6-27B with 262K context and 55 tok/s
A setup using four 5060 Ti GPUs (totaling $1800) achieves 55 tokens per second with Qwen3.6-27B-FP8, supporting 262K context length and bfloat16 KV cache. The configuration uses P2P and FlashInfer, with benchmark results showing 55.67 output token throughput and 65.25% speculative decoding acceptance rate.
GLM-5.2 (744B, 2-bit) achieves 7.3 tok/s on 4×3090 with 192GB RAM
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.
DiffusionGemma 26B on 4090 reaches 475t/s with limitations
DiffusionGemma 26B runs at up to 475t/s on a 4090 via vLLM with INT4 AWQ quantization, achieving speeds between 290t/s and 700t/s based on output length. However, it suffers from single-user operation, lower response accuracy, rapid context loss, and slower time-to-first-token compared to standard 26B models.
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.