Meta AILlama 2 70B Chat Hf
Meta AI's Llama 2 70B Chat Hf is a fine-tuned generative text model optimized for dialogue use cases, with a context window of 4,096 tokens and capabilities including function calling, JSON mode, and text generation. It utilizes an optimized transformer architecture with supervised fine-tuning and reinforcement learning with human feedback.
Input
Output
Context
4K
Max Output
2K
Parameters
69B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Benchmarks
Resources & Links
Estimated GPU requirements for Llama 2 70B Chat Hf
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 2 70B Chat Hf
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Llama 2 70B Chat Hf advertises a context window of 4,096 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. Llama 2 70B Chat Hf is an open-weight model released under the Llama2 license, so the weights can be downloaded and self-hosted within that license's terms.
Llama 2 70B Chat Hf 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.
Llama 2 70B Chat Hf has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured Llama 2 70B Chat Hf is shown as not available rather than estimated.
Not on the managed pool today; Llama 2 70B Chat Hf 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 67.4 GB at INT8 for the weights and a default context, from the catalog's 69B 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: Oct 23, 2026
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