Z.aiCogVLM 2 Llama 3 Chat 19B
CogVLM2 Llama 3 Chat 19B is a 19-billion parameter chat model built by Z.ai, based on Meta-Llama-3-8B-Instruct. It is genuinely best at image understanding tasks, showing significant improvements on benchmarks like TextVQA and DocVQA. A notable technical trait is its support for high-resolution image inputs up to 1344x1344 pixels.
Input
Output
Context
8K
Max Output
2K
Parameters
19.5B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Estimated GPU requirements for CogVLM 2 Llama 3 Chat 19B
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 CogVLM 2 Llama 3 Chat 19B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
CogVLM 2 Llama 3 Chat 19B advertises a context window of 8,192 tokens, with a maximum output of 2,048 tokens in a single response. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. CogVLM 2 Llama 3 Chat 19B is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
CogVLM 2 Llama 3 Chat 19B 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 CogVLM 2 Llama 3 Chat 19B 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; CogVLM 2 Llama 3 Chat 19B 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 19.6 GB at INT8 for the weights and a default context, from the catalog's 19.5B 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, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Nov 4, 2026
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