Codellama 34B Instruct Hf
Developed by Meta AI, Codellama 34B Instruct Hf is a chat model designed for general code synthesis and understanding, exceling at tasks such as code completion and instructions. It features a context window of 16,384 tokens, allowing it to process and respond to lengthy inputs.
Input
Output
Context
100K
Max Output
16K
Parameters
33.7B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Resources & Links
Estimated GPU requirements for Codellama 34B Instruct 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 Codellama 34B Instruct Hf
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Codellama 34B Instruct Hf advertises a context window of 100,000 tokens, with a maximum output of 16,384 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. Codellama 34B Instruct 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.
Codellama 34B Instruct 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.
No published benchmark result for Codellama 34B Instruct Hf 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; Codellama 34B Instruct 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 33.1 GB at INT8 for the weights and a default context, from the catalog's 33.7B 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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