Deploy meta-llama/Meta-Llama-3-70B-Instruct | Inference Endpoints by Hugging Face

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Deploy Meta-Llama-3-70B-Instruct

Catalog model officially supported by Inference Endpoints.

This model is from our Inference Catalog and comes with an optimized configuration.

Endpoint Name

Input a name for your new endpoint

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Commit Revision

Specify a revision commit hash for the Hugging Face repository

optional

Task

Select a supported task with the right inputs and outputs for your model pipeline, or define a custom task.

Download Pattern

Specify what file type(s) should be downloaded from the repository.

Automatic Scale-to-Zero

Endpoints scaled to 0 replicas are not billed. They may take some time to scale back up once they start receiving requests again.

Number of replicas

Automatically scale the number of replicas within Min and Max based on compute usage. Min is always 0 if Scale-To-Zero is active.

Min Max

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Autoscaling Strategy

Control what type of trigger will cause your Endpoint to scale up.

Hardware Usage Pending Requests

Hardware Utilization Threshold (%)

A scale up event will be triggered if the average hardware utilisation (%) exceeds this threshold for more than 20 seconds.

Protected Verified Public Private

A protected Endpoint is available from the Internet, secured with TLS/SSL and requires a valid Hugging Face Token for Authentication.

Container Type

The Default container is the easiest way to deploy endpoints, and is very flexible thanks to custom Inference Handlers. You can also select a container optimized for Text-Generation inference, or link your own Custom container.

Custom Cuda Kernels

TGI uses custom kernels to speed up inference for some models. You can try disabling them if you encounter issues.

Quantization

Quantization can reduce the model size and improve latency, with little degradation in model accuracy.

Max Input Length (per Query)

Increasing this value can impact the amount of RAM required. Some models can only handle a finite range of sequences.

optional

Max Number of Tokens (per Query)

The larger this value, the more memory each request will consume and the less effective batching can be.

optional

Max Batch Prefill Tokens

Number of prefill tokens used during continuous batching. It can be useful to adjust this number since the prefill operation is memory-intensive and compute-bound.

optional

Max Batch Total Tokens

Number of tokens that can be passed before forcing waiting queries to be put on the batch. A value of 1000 can fit 10 queries of 100 tokens or a single query of 1000 tokens.

optional

Default Env

Environment variables that will be provided to your container during deployment.

Key

Value

Secret Env

Same as Default, but people with access to this endpoint will not be able to read these values after creation.

Key

Value