Overview: Why Medium-Sized Models?

Overview: Why Medium-Sized Models?#

A medium LLM typically runs on a single node with 4-8 GPUs. It offers a balance between performance and efficiency. These models provide stronger accuracy and reasoning than small models while remaining more affordable and resource-friendly than very large ones.

Model Size Comparison#

Let’s understand how different model sizes compare:

Model Size

Parameters

Memory (FP16)

Typical Use Case

Hardware Requirements

Small

7B-13B

14-26 GB

Prototyping, simple tasks

1-2 GPUs

Medium

70B-80B

140-160 GB

Production workloads, complex reasoning

4-8 GPUs

Large

400B+

800+ GB

Research, maximum capability

Multiple nodes

Why Choose Medium-Sized Models?#

Advantages:

  • Balanced Performance: Strong accuracy and reasoning capabilities

  • Cost-Effective: More affordable than very large models

  • Resource Efficient: Can run on single-node multi-GPU setups

  • Production Ready: Ideal for scaling applications where large models would be too slow or expensive

Perfect for:

  • Production workloads requiring good quality at lower cost

  • Applications needing stronger reasoning than small models

  • Scaling scenarios where large models are too resource-intensive

Our Example: Llama-3.1-70B#

In this tutorial, we’ll deploy Meta’s Llama-3.1-70B-Instruct model, which:

  • Has 70 billion parameters

  • Requires ~140GB memory in FP16 precision

  • Needs 4-8 GPUs for efficient serving

  • Provides excellent reasoning and instruction-following capabilities

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