Llama-3.1-8B-Instruct

Updated
25.06.2026
Tools
Reasoning
Code
Multilingual
Run

huggingface-cli download meta-llama/Llama-3.1-8B-Instruct
from transformers import AutoModel
model = AutoModel.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
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At a glance
  • License: Llama3.1
  • Context length: 128K tokens
  • Languages: Multilingual
  • Minimum hardware: ~18 GB VRAM
  • Strengths: general chat, tool use and multilingual tasks
Overview
Llama-3.1-8B-Instruct is an 8-billion-parameter instruction-tuned model from Meta (meta-llama). It uses a dense decoder-only transformer architecture, so all 8B parameters are active on every token, and it ships with a 128K context window. The "instruct" tuning means Meta trained the base model further with supervised fine-tuning and RLHF to follow instructions and hold a conversation.
In Atomic Chat the model runs fully on your own hardware. Weights load on-device, inference happens locally, and once the download finishes nothing leaves your machine. That makes it a practical choice for private notes, offline work on a laptop, or any task where you don't want prompts going to a remote API.
What it is good at
The model carries capability tags for tools, reasoning, code, and multilingual text, which maps to a few concrete jobs:
  • Local coding help — writing functions, explaining a stack trace, and refactoring snippets without sending source code to a cloud service.
  • Long-document work — the 128K context fits large files or long chat histories, so you can summarize or query a sizeable document in one pass.
  • Tool calling and structured output — the model can emit function calls and JSON, which lets it drive small agent loops or extraction tasks that run entirely on your device.
Running it locally
At 8B parameters the model is reachable on consumer hardware. A 4-bit quantized build (Q4_K_M) is roughly 5 GB and runs on a GPU with about 6 GB of VRAM; full 16-bit weights want closer to 16 GB. No GPU is fine too — on CPU with 16 GB+ of system RAM you'll get a few tokens per second. The full 128K context needs extra memory for the KV cache, so keep headroom if you push the window.
huggingface-cli download meta-llama/Llama-3.1-8B-Instruct
From there you can load it with Hugging Face Transformers, serve it with vLLM, or skip the setup and run it through Atomic Chat with a one-click download.
License
Llama-3.1-8B-Instruct is released under the llama3.1 community license. It permits commercial and research use, including fine-tuning and deploying the model in products. The license adds Meta's acceptable-use terms and an attribution requirement, plus a clause for very large-scale deployments, so read it before shipping at scale.
Desktop



macOS
(M1 or better)
Download
Windows
(x64)
Download
Linux
(x86_64)
Download
Frequently asked questions
Llama-3.1-8B-Instruct is an 8-billion-parameter open-weight language model from Meta. It is the instruction-tuned variant of Llama 3.1 8B, refined with supervised fine-tuning and RLHF so it follows prompts and chats naturally. It handles chat, writing, summarizing, and coding, and supports a 128K-token context window.
A 4-bit quantized build (Q4_K_M) is about 5 GB and runs on a GPU with roughly 6 GB of VRAM, with an RTX 3060 12GB being a comfortable target. Full 16-bit weights need closer to 16 GB. With no dedicated GPU you can still run it on CPU with 16 GB or more of system RAM at a few tokens per second.
Yes. The weights are open and free to download under Meta's llama3.1 community license, which allows commercial and research use. In Atomic Chat there is no per-token cost because the model runs on your own machine. You only pay for the hardware and electricity you already own.
Yes. After the weights download once, the model runs entirely on-device with no internet connection needed. Atomic Chat loads it locally, so prompts and responses stay on your machine. This makes it usable on a plane, in an air-gapped setup, or anywhere you want to keep data private.
It is a strong general-purpose model for its size, good at conversation, summarizing, content generation, and coding help. Its capability tags cover tools, reasoning, code, and multilingual text, and the 128K context lets it work over long documents in one pass. The 8B size keeps it fast enough for real-time use on a laptop or a mid-range GPU.