Llama 3.1 8B Instruct
Optimized for multilingual dialogue, outperforming open-source and closed chat models on industry benchmarks.
Meta 
Amazon Bedrock Multilingual instruction-tuned model with 128K context
Llama 3.1 8B Instruct is an 8-billion-parameter instruction-tuned text generation model developed by Meta, part of the Llama 3.1 collection that also includes 70B and 405B variants. It accepts text input and produces text output, and is built on a multilingual foundation designed to handle dialogue across multiple languages. The model is available through Amazon Bedrock, making it accessible via managed cloud infrastructure without requiring self-hosted deployment.
This model is optimized specifically for multilingual dialogue use cases, making it well-suited for conversational applications, question answering, summarization, and instruction-following tasks. With a 128,000-token context window, it can process and respond to long documents or extended conversations in a single pass. Its 8B parameter size makes it a practical choice for applications where inference cost and latency are considerations alongside capability.
What Llama 3.1 8B Instruct supports
Multilingual Dialogue
Handles conversational tasks across multiple languages, optimized through instruction tuning for dialogue-specific use cases.
Long Context Processing
Supports a 128,000-token context window, enabling processing of long documents or extended multi-turn conversations in a single request.
Instruction Following
Fine-tuned to follow natural language instructions, making it suitable for task completion, summarization, and structured response generation.
Text Summarization
Condenses long-form text into concise summaries, leveraging the large context window to handle lengthy source documents.
Code Assistance
Capable of generating, explaining, and debugging code across common programming languages as part of its general instruction-following training.
Question Answering
Responds to factual and open-ended questions using knowledge encoded during pretraining, with a knowledge cutoff of early 2023.
Ready to build with Llama 3.1 8B Instruct?
Get Started FreeCommon questions about Llama 3.1 8B Instruct
What is the context window for Llama 3.1 8B Instruct?
The model supports a context window of 128,000 tokens, allowing it to process long documents or extended conversations in a single request.
What is the knowledge cutoff date for this model?
The training date is listed as n/a in the available metadata. Based on publicly available information from Meta, Llama 3.1 models have a knowledge cutoff of approximately early 2023.
Is this model available for self-hosting?
This specific listing (llama-3.1-8b-instruct-bedrock) is hosted on Amazon Bedrock. Meta also releases Llama 3.1 8B weights publicly, allowing self-hosted deployment for those who prefer it.
What input and output types does this model support?
Llama 3.1 8B Instruct accepts text input and produces text output. It does not natively support image, audio, or video inputs.
What languages does this model support?
The model is described by Meta as multilingual and is optimized for multilingual dialogue. Meta's documentation lists support for English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai among the primary supported languages.
How does the 8B variant relate to the other Llama 3.1 models?
Llama 3.1 8B Instruct is the smallest model in the Llama 3.1 instruction-tuned collection, which also includes 70B and 405B parameter variants. All three share the same multilingual, instruction-tuned design.
What people think about Llama 3.1 8B Instruct
Community discussions on r/LocalLLaMA frequently include Llama 3.1 8B in comparative benchmarks and hardware experiments, with users noting it as a commonly referenced baseline in the 7B–9B parameter class. Threads exploring model personality via hidden state probing and large-scale task benchmarking across dozens of models often include it as a data point.
Some community members use it to evaluate what fits within specific VRAM constraints, such as 32GB setups, and it appears in discussions about running multiple models on high-end consumer or workstation hardware. Concerns in these threads tend to focus on how smaller models in this size class compare on specific task types rather than on Llama 3.1 8B specifically.
I locally benchmarked 41 open-source LLMs across 19 tasks and ranked them
4x AMD R9700 (128GB VRAM) + Threadripper 9955WX Build
LLMs grading other LLMs 2
I measured the "personality" of 6 open-source LLMs (7B-9B) by probing their hidden states. Here's what I found.
I gave the same silly task to ~70 models that fit on 32GB of VRAM - thousands of times (resharing my post from /r/LocalLLM)
Documentation & links
Parameters & options
Explore similar models
Meta Llama 4 Scout
Llama 4 Scout is a powerful multimodal model with 17 billion active parameters, offering state-of-the-art performance in its class.
Meta Llama 4 Scout
Llama 4 Scout is a powerful multimodal model with 17 billion active parameters, offering state-of-the-art performance in its class.
Meta Llama 4 Maverick
Llama 4 Maverick is a high-performance multimodal model with 17 billion active parameters, supporting 12 languages and offering industry-leading image and text understanding.
Google Gemini 3.1 Pro
Google's frontier reasoning model delivering enhanced software engineering, agentic reliability, and multimodal intelligence across a 1M-token context window.
OpenAI GPT‑5.2 Pro
The most capable model series yet for professional knowledge work.
Nvidia Nemotron 3 Nano 30B
NVIDIA's compact open-weight hybrid MoE model with 30B total / 3.5B active parameters, delivering strong reasoning and coding performance up to 1M context.
Start building with Llama 3.1 8B Instruct
No API keys required. Create AI-powered workflows with Llama 3.1 8B Instruct in minutes — free.