Chatglm 2 6B
ChatGLM2-6B is a second-generation open-source bilingual Chinese-English dialogue model developed by Z.ai. It excels at multi-turn dialogue and shows substantial performance improvements over its predecessor on benchmarks like MMLU and GSM8K. A notable technical trait is its use of FlashAttention and Multi-Query Attention to achieve a 32K-token context window and more efficient inference.
Input
Output
Context
33K
Max Output
8K
Parameters
6B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Chatglm 2 6B
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Chatglm 2 6B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Chatglm 2 6B advertises a context window of 32,768 tokens, with a maximum output of 8,192 tokens in a single response. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Chatglm 2 6B is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Chatglm 2 6B accepts Text and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Chatglm 2 6B is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Chatglm 2 6B is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 6.2 GB at INT8 for the weights and a default context, from the catalog's 6B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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