Codegeex 4 All 9B GGUF
CodeGeeX4-ALL-9B is an open-source multilingual code generation model from Z.ai, continually trained on GLM-4-9B. It is designed for comprehensive software development scenarios, including code completion, generation, interpreter functions, web search, function call, and repository-level Q&A. A notable technical trait is its exceptionally large context window of 131,072 tokens.
Input
Output
Context
131K
Max Output
-
Parameters
9.4B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Codegeex 4 All 9B GGUF
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 Codegeex 4 All 9B GGUF
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Codegeex 4 All 9B GGUF advertises a context window of 131,072 tokens. 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. Codegeex 4 All 9B GGUF is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
Codegeex 4 All 9B GGUF 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 Codegeex 4 All 9B GGUF 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; Codegeex 4 All 9B GGUF 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 9.6 GB at INT8 for the weights and a default context, from the catalog's 9.4B 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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