Megathread for discussing the release of GLM-5.3-Flash.

• Quants
• Fine-Tunes & Abliterations
• Chat Templates
• Inference Server Support & Configuration
• Experiences, Benchmarks & Model Comparisons

We'll try to clean up future duplicates around the release and point them here.

Highlights

GLM-5.3-Flash is the first natively multimodal model in the GLM-5 series, and the first open-weight release of the glm5_next architecture. Z.ai's pitch: outperforms GLM-5.2 at one-tenth the price while approaching Claude Opus 4.8 on coding and agentic benchmarks. It introduces:

• Hybrid Sparse + Linear Attention: 45 layers laid out as repeating blocks of 3x KDA linear attention followed by 1x DeepSeek-style sparse attention (34 linear / 11 sparse layers). The sparse layers use a lightning indexer (32 heads, dim 128) with a top-k budget of 2048 tokens, sharply reducing long-context serving cost.
• Manifold-Constrained Hyper-Connections (mHC): widened residual streams with manifold-constrained mixing between layers, adopted to further improve scaling efficiency.
• Natively Multimodal: a 24-layer ViT (448px, patch 14, 2x2 spatial merge) with temporal patching, so image and video tokens are in the vocabulary. Trained on a 30T-token multimodal corpus.
• MTP head shipped in the weights: 1 next-N prediction layer; the official vLLM recipe uses it with 5 speculative tokens.
• FP8 first: the main repo is FP8 (e4m3, dynamic activation scaling). A separate official BF16 repo exists.

Model Overview

• Type: Causal Language Model with Vision Encoder (Glm5NextForConditionalGeneration)
• Training Stage: Pre-training (30T multimodal tokens) & Post-training
• License: MIT

Language Model

• Number of Parameters: 320B with 18B activated
• Hidden Dimension: 4096
• Vocabulary: 154,880
• Number of Layers: 45 (first 3 dense MLP, remaining 42 MoE)
• Hidden Layout: 11 x (3 x (KDA Linear Attention -> MoE) -> 1 x (Sparse Attention -> MoE)), plus 1 trailing linear layer
• KDA Linear Attention (34 layers):
  • Number of Heads: 64
  • Head Dimension: 128
• Sparse Attention (11 layers, DeepSeek-style):
  • Number of Attention Heads: 64
  • QK / V Head Dimension: 256 / 256
  • Indexer: 32 heads, head dim 128
  • Budget: top-2048 tokens
• Mixture of Experts:
  • Number of Experts: 288 routed + 1 shared
  • Number of Activated Experts: 8 routed + 1 shared
  • Expert Intermediate Dimension: 2048
  • Dense Intermediate Dimension (layers 0-2): 12288
• mHC: enabled
• MTP: 1 layer
• Context Length: 1,048,576 tokens (max_position_embeddings); evaluated at 300K text / 164K vision

Vision Encoder

• Depth: 24 layers, hidden 1024, 16 heads
• Image Size: 448 x 448, Patch Size: 14
• Spatial Merge: 2 x 2, Temporal Patch: 2 (video)
• Output Projection: 4096 (LM hidden)

Weights

• zai-org/GLM-5.3-Flash - FP8 (e4m3, dynamic), 62 shards, ~331 GB
• zai-org/GLM-5.3-Flash-BF16 - BF16, 120 shards, ~640 GB

[ARCHITECTURE IMAGE - upload via the editor here, or use the link below]

Architecture diagram

Benchmarks

[BENCHMARKS IMAGE - upload via the editor here, or use the link below]

Benchmark chart

Recommended sampling parameters

From generation_config.json and the eval setup on the model card:

• Default: temperature=1.0, top_p=0.95 (max generation length 163,840 for evals)
• Agentic coding (NL2Repo): temperature=1.0, top_p=1.0
• DeepSWE: temperature=0.95, top_p=1.0
• Vision (BabyVision): temperature=1.0, top_p=0.95

Inference

vLLM (official recipe, vLLM 0.27.0+, FlashInfer 0.6.17+ for NoPE sparse MLA, Hopper and newer):

vllm serve zai-org/GLM-5.3-Flash \ --tensor-parallel-size 4 \ --kv-cache-dtype fp8 \ --speculative-config '{"method":"mtp","num_speculative_tokens":5}' \ --tool-call-parser glm47 \ --reasoning-parser glm45 \ --enable-auto-tool-choice \ --served-model-name zai-org/GLM-5.3-Flash 

SGLang: official cookbook has verified configs for H100/H200/B200/B300/GB200/GB300 (TP4/EP4), with adaptive MTP for low-latency and --mm-feature-transport cpu to offload vision features.

Also supported at launch: TokenSpeed, KTransformers (CPU/GPU hybrid tutorial linked below).

Official Links

• HF: https://huggingface.co/zai-org/GLM-5.3-Flash
• HF (BF16): https://huggingface.co/zai-org/GLM-5.3-Flash-BF16
• MS: https://modelscope.cn/models/ZhipuAI/GLM-5.3-Flash
• Repo: https://github.com/zai-org/GLM-5
• Blog: https://z.ai/blog/glm-5.3-flash
• Docs: https://docs.z.ai/guides/llm/glm-5.3-flash
• Paper: https://arxiv.org/abs/2602.15763
• vLLM: https://recipes.vllm.ai/zai-org/GLM-5.3-Flash
• SGLang: https://docs.sglang.io/cookbook/autoregressive/GLM/GLM-5.3-Flash
• TokenSpeed: https://lightseek.org/tokenspeed/recipes/models#glm-5-3-flash
• KTransformers: https://github.com/kvcache-ai/ktransformers/blob/main/doc/en/kt-kernel/GLM-5.3-Flash-Tutorial.md
• Discord: https://discord.gg/QR7SARHRxK

Popular

• Unsloth GGUF: https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF
• Unsloth FP8: https://huggingface.co/unsloth/GLM-5.3-Flash-FP8
• AtomicChat GGUF: https://huggingface.co/AtomicChat/GLM-5.3-Flash-GGUF