GLM-5.3-Flash
| property | value |
|---|---|
| parameters | ~321B total, 18B active per token |
| layers | 45 text, plus a 24-layer vision encoder |
| attention | KDA linear on ~34 layers, NoPE sparse MLA with a top-2048 DSA indexer on every 4th layer |
| MoE | 288 routed experts, top-8 per token |
| context | 1,048,576 tokens |
| speculation | native MTP draft layer; DFlash2 external drafter also published |
Reusable state has two parts. Sparse MLA KV grows with sequence length. KDA recurrent state is fixed size per sequence and cannot be reconstructed from KV.
Weight formats
| format | size | fits 1 node (123.73 GiB) | fits 2 |
|---|---|---|---|
| FP8 native | ~306 GiB | no | no |
| NVFP4, 4-bit experts | ~182 GiB | no | yes |
| EXL3 4bpw | ~164 GiB | no | yes |
No format fits one node, so any engine for this fuel is split across at least two.
On disk here: Mia-AiLab/GLM-5.3-Flash-EXL3-TR3-4bpw (164 GB) and incoai/GLM-5.3-Flash-DFlash2 (2.2 GB drafter).
What it demands
18B active parameters at ~4.25 bits is about 9.6 GB read per token if every active weight is re-read. Against one node at 273 GB/s that is a ~28 tok/s single-stream ceiling. Two nodes working the same token halve the per-node read and roughly double it.
Only 8 of 288 experts fire per token, so tokens that share experts can amortize the read. That is where aggregate throughput above the single-stream ceiling comes from.
Engines
None yet.