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glm-5.3-flash
Repository: localaiLicense: mit
glm-5.3-flash
# GLM-5.3-Flash 👋 Join our WeChat or Discord community. 📖 Check out the GLM-5.3-Flash blog and GLM-5 Technical report. 📍 Use GLM-5.3-Flash API services on Z.ai API Platform. ## Introduction We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply reducing long-context serving costs while preserving precise long-context capabilities. The model also adopts Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency. Together with our latest 30T-token multimodal pre-training corpus, these changes enable GLM-5.3-Flash to deliver more intelligence with less compute. ## Serve GLM-5.3-Flash Locally ...
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glm-5.3-flash-q4
Repository: localaiLicense: mit
glm-5.3-flash-q4
GLM-5.3-Flash is Z.ai's natively multimodal 320B-parameter mixture-of-experts model with 18B active parameters. It combines sparse and linear attention for coding, agentic work, tool use, vision, and long-context tasks. This entry uses the UD-Q4_K_XL GGUF quantization and enables the model's MTP speculative-decoding head.
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glm-5.3-flash-q8
Repository: localaiLicense: mit
glm-5.3-flash-q8
GLM-5.3-Flash is Z.ai's natively multimodal 320B-parameter mixture-of-experts model with 18B active parameters. It combines sparse and linear attention for coding, agentic work, tool use, vision, and long-context tasks. This entry uses the higher-quality Q8_0 GGUF quantization and enables the model's MTP speculative-decoding head.
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glm-5.3
Repository: localaiLicense: other
glm-5.3
# GLM-5.3 GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks: + Stronger Coding: GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam. + Emergent Cyber Capability: As we scaled post-training, cyber capability developed faster than we expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks. ## Benchmark ### Serve GLM-5.3 Locally GLM-5.3 supports deployment with the following frameworks. Feel free to try them out: - SGLang — see cookbook - vLLM — see recipes - TokenSpeed — see here - Transformers — see transformers docs - KTransformers — see tutorial - Unsloth — see guide - For deployment on the `Ascend NPU` platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here. ### Note ...
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