Llama 3.2 1B Instruct Spinquant_int 4_EO8
Meta AI builds Llama 3.2 1B Instruct Spinquant_int 4_EO8, a 1.23-billion parameter multilingual chat model optimized for dialogue, agentic retrieval, and summarization. It is a quantized version of the instruction-tuned model, trained on a mix of public online data up to December 2023. A notable technical trait is its 128,000-token context window.
Input
Output
Context
128K
Max Output
2K
Parameters
1B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Llama 3.2 1B Instruct Spinquant_int 4_EO8
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 Llama 3.2 1B Instruct Spinquant_int 4_EO8
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Llama 3.2 1B Instruct Spinquant_int 4_EO8 advertises a context window of 128,000 tokens, with a maximum output of 2,048 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. Llama 3.2 1B Instruct Spinquant_int 4_EO8 is an open-weight model released under the Llama3.2 license, so the weights can be downloaded and self-hosted within that license's terms.
Llama 3.2 1B Instruct Spinquant_int 4_EO8 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 Llama 3.2 1B Instruct Spinquant_int 4_EO8 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; Llama 3.2 1B Instruct Spinquant_int 4_EO8 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 1.5 GB at INT8 for the weights and a default context, from the catalog's 1B 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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