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Llama 3.1 70B Instruct

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Llama 3.1 70B Instruct is a mid-size open-weights instruct model balancing quality and deployability on a single large GPU or small multi-GPU nodes.

Best for:On-prem chat for regulated industriesCost tier:70b

Open weights LLM · Release · Llama 3.1 Community License

open-weightsself-host

Updated 1 day ago · Verified Apr 2026 · Score 78

Decision summary

Why teams reach for it, where it fits, and what to watch for — before you dive into specs.

Why teams choose it

  • Quantized deployments (4/8-bit) are common—track eval drift vs fp16.
  • License requires acceptable use review for commercial redistribution.

Best use cases

  • Use this when on-prem chat for regulated industries
  • Use this when fine-tunes on proprietary documents

Tradeoffs

  • Still weaker than frontier closed models on hardest tasks.
  • Ops overhead for monitoring and safety layers.

Technical details

Modalities, benchmarks, and release context.

Modalities

What goes in and what comes out.

Inputs
text
Outputs
text
Capabilities
reasoning, coding, fine-tuning
Release: ·License: Llama 3.1 Community License

Benchmarks snapshot

Structured JSON for reproducible comparisons.

No benchmark data yet — see comparisons for relative performance.

Family lineup

Explore other versions in this family after you have the headline on this model.

Continue exploring

A short set of comparisons, nearby models, and links to go deeper — without repeating the same paths.

This page is based on publicly available documentation, benchmarks, and real-world usage patterns. Last reviewed for accuracy recently.