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// GLM / CogVideoX (Z.ai) · Speech-to-text

GLM-ASR-Nano-2512: VRAM & local deployment

2.26B parameters. About 2.0 GB at 4-bit and 5.5 GB at BF16.

released 2025-12-09licence: Commercial OKmemory figures are estimates

01Specs

PublisherZ.ai (Zhipu) · zai-org
Family / seriesGLM / CogVideoX (Z.ai) · GLM-ASR-Nano
TypeSpeech-to-text (automatic-speech-recognition)
Parameters2.26B Source: counted from official safetensors weights
Released weightsBF16 · 4.5 GBdownload
LicenceMIT · Commercial OKPermissive licence (Apache-2.0 / MIT / CC-BY / OpenMDW): commercial use allowed.
Released2025-12-09 (Hugging Face repo created)

02How much VRAM it needs (estimate)

Speech models are small; the weight size is close to the total memory needed.

PrecisionWeightsWeights + overheadSmallest GPUMac
BF16 / FP16 (original)4.5 GB 5.5 GB 8GB GPUMac 16GB
FP8 / INT8 / Q8_02.4 GB 3.1 GB 8GB GPUMac 16GB
4-bit (Q4_K_M / AWQ / GPTQ-Int4)1.4 GB 2.0 GB 8GB GPUMac 16GB

Bits per weight: BF16 16, FP8 8.5, 4-bit 4.85. “Fits” = total ≤ 90% of memory; Mac GPU can use ~2/3 of unified memory (≤36GB) or ~3/4 (larger). For MoE, all experts must be in memory, so size by total parameters. Method →

03How to run it

Only runtimes the publisher or the runtime’s official library names for this model.

Which inference engine to choose →

04Official repositories & quantised builds

1 official repo(s) from zai-org.

05Other GLM / CogVideoX (Z.ai) models

All 35 →
ModelParams4-bit totalReleased
GLM-5.3753.3B504 GB2026-08-25
GLM-5.3-Flash320B / 18B214 GB2026-08-25
GLM-5.2753.3B504 GB2026-06-16
SCAIL-2——2026-06-09
GLM-5.1753.9B504 GB2026-04-03
GLM-5744B / 40B497 GB2026-02-11
GLM-OCR1.32B1.9 GB2026-01-30
GLM-4.7-Flash31.2B / 3B21.8 GB2026-01-19

06Sources

Data checked 2026-04-07. Parameters, context and licence are copied from the official repository; memory figures are our estimates.

Data updated: Self-host open models(UTC+8)