glm

This article details the latest progress of GLM-5.2 in Agentic RL, including the introduction of slime infrastructure, shifting from GRPO to PPO for handling long trajectories, and an online anti-cheat mechanism; it also explores Qwen's research on verifier quality, proposing three dimensions of scalability, faithfulness, and robustness, and designs multiple verification strategies for different tasks to improve the reliability of reward signals.

DeepSeek releases version 4 of its GLM model, version 5.2 PRO.

GLM 5.2, a new version of the GLM language model, has been released, demonstrating improved performance.

Moonshot AI's Kimi and Zhipu AI's GLM have achieved notable results on frontier code benchmarks.

GLM 5.2 is optimized for CPU-only inference on AMD Epyc processors with 512GB RAM.

A hobbyist compares a heavily quantized GLM 5.2 (Q1_S) against a high-quant Qwen 27B (Q8) on a code generation task, finding that the lower-quant larger model significantly outperforms the higher-quant smaller model in quality and completeness.

Zhipu AI launches a free tier for GLM 5.2 with usage limits.

A quantized GGUF version of the abliterated GLM-5.2 model is released on Hugging Face, enabling local inference with various tools like Transformers, llama.cpp, and vLLM.

GLM 5.2 post-training code is open-sourced, using Megatron-LM for training and SGLang for rollout generation, forming a continuous RL loop with synchronized weights.

TileRT is a tile-based runtime achieving ultra-low-latency LLM inference, with recent milestones including 1000+ tokens/s on a 1-trillion-parameter model. It supports models like DeepSeek-V3.2 and GLM-5, and is available as open-source on GitHub.

NVIDIA has released an NVFP4 precision version quantized from the Zhipu GLM-5.2 model, available via the Hugging Face free tier API.

GLM 5.2, an open AI model, is now available in the Cursor coding tool via a partnership with Fireworks.

A user discusses a locked Dell quote for 6x RTX PRO 6000 Max-Q GPUs at a discounted price to build an inference cluster for GLM 5.2, asking the community for advice on purchasing strategy before the quote expires.

A comparison stating GPT 5.5 outperforms GLM 5.2, but GLM 5.2 outperforms Opus 4.8.

Running GLM5.2 with 7 trillion tokens on a budget setup using 4x RTX 3090 GPUs and 192GB RAM.

The author calculates the token cost and break-even period of running large models on a Mac Studio, concluding that it is not cost-effective for ordinary users to buy a Mac for personal large model use, and suggests that using APIs or renting GPUs is more economical.

A researcher suggests it's time to buy more GPUs and build a local AI stack, referencing Qwen 3.5 27B and GLM 5.2 as models that cancel the threat of a permanent underclass.

The tweet highlights that GLM 5.2 and Kimi 2.7 are available without limits in a Devin subscription, describing it as a gold mine.

Antirez reports the first working implementation of GLM 5.2 in DwarfStar, using a 433 GB GGUF file on an M3 Ultra with 512GB RAM, though it needs further refinement.

Natolambert shares a positive first impression of GLM, noting it is easy to set up on Fireworks AI and works well with Claude Code.