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.

Text generation

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GLM-5.3-NVFP4

local-inference-lab

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Text generation

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GLM-5.3-NVFP4-CSF

local-inference-lab

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Text generation

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Qwen3.8-Flash-Next-NVFP4

local-inference-lab

local-inference-lab/Qwen3.8-Flash-Next-NVFP4 is a mixed-precision model distilled from Qwen/Qwen3.8-Flash-Next using quantization-aware distillation (QAD). The student is trained against the original BF16 teacher with quantized weights in its forward pass, learning to compensate for quantization error rather than relying on post-training quantization alone. The architecture is unchanged: 48 decoder layers, 512 routed experts per layer with 10 active per token, hybrid Gated DeltaNet/Qwen Sparse Attention (QSA), and n-gram embedding tables. Compression comes from lower-precision weights, not fewer layers or experts. The checkpoint occupies approximately 98 GiB on disk and is particularly suited to run on a single RTX 6000 (with PLE offload), or a DGX Spark (with or without PLE offload). NVFP4 stores 4-bit E2M1 values with FP8 block scales per 16 elements and an FP32 global scale. MXFP8 stores 8-bit E4M3 values with power-of-two block scales per 32 elements. Residual-mixing and PLE normalization weights are trained in FP32 and exported in BF16. Shared-expert scalar gates remain frozen in BF16.

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Qwen3.8-Flash-Next-NVFP4-MXFP8-CSF-QAD

local-inference-lab

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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DeepSeek-V4.1-Flash-1017-lossless-CSF

GLM-5.2-NVFP4

GLM-5.3-Flash-DFlash2

GLM-5.3-Flash-NVFP4

GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD

GLM-5.3-Flash-NVFP4-Spark

GLM-5.3-NVFP4

GLM-5.3-NVFP4-CSF

Qwen3.8-Flash-Next-NVFP4

Qwen3.8-Flash-Next-NVFP4-MXFP8-CSF-QAD

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