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GLM 5.2 FP8

GLM 5.2 FP8 is an FP8 quantized text-generation model from zai-org, released under the MIT license. Its official model card describes it as a flagship model for long-horizon tasks with a solid 1M-token context, improved coding, an IndexShare architecture, and MTP speculative decoding. The card lists SGLang, vLLM, Transformers, and KTransformers as deployment frameworks, and links to blog, paper, API, and community resources.

EstimatedRepository opened Jun 16, 2026Source checked 9/24/2026Version: f33c6dc5
LOCALRENTED GPUOPEN WEIGHTS
Freshness 63 out of 100. Repository age: 121 days old, Jun 16, 2026. Measures age, not quality.
Popularity 70 out of 100. 196,139 downloads in 30 days; 268 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source
Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source
Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method
WORKFLOWS FOR THIS MODEL 1

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At a glance

Parameters
753.33B
Architecture
glm_moe_dsa
Context length
1,048,576
License
mit
Disk space
703.7 GB
Software
SGLang, vLLM
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Best for

  • Long-horizon task processing with up to 1M-token context
  • Text generation
  • Coding tasks with adjustable thinking effort levels
  • Agentic tool use and MCP-style workflows
  • Local deployment via supported frameworks such as SGLang, vLLM, Transformers, or KTransformers

Tradeoffs

  • The supplied source data is truncated and does not include complete framework version or hardware requirements.
  • The model card marks a new version (zai-org/GLM-5.3), but no migration or compatibility details are provided.
  • Only FP8 model facts are supplied; other precision or format options are not described.
  • No independent testing, performance benchmarks, or real-world validation are provided in the source data.
  • No information is given about supported languages beyond the metadata tags 'en' and 'zh'.

Ways to run it

SGLang · Linux / rented GPU

Publisher-linked deployment recipe; follow the maintained runtime instructions at the source.

Official instructions

vLLM · Linux / rented GPU

Publisher-linked deployment recipe; follow the maintained runtime instructions at the source.

Official instructions

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