Llama 3 8B Instruct
Best-in-class 8B model with improved reasoning, code generation, and instruction following.
Meta 
Amazon Bedrock Instruction-tuned 8B model for reasoning and code
Llama 3 8B Instruct is an 8-billion-parameter instruction-following language model developed by Meta. It is part of the Llama 3 family, which also includes a 70B variant, and is served on MindStudio via Amazon Bedrock. The model was trained with improvements to both pretraining and post-training procedures, resulting in reduced false refusal rates, improved alignment, and more diverse responses. Its context window supports up to 8,000 tokens.
This model is designed for tasks that benefit from instruction following, including code generation, reasoning, and general-purpose text generation. Post-training refinements made the model more steerable, meaning it responds more reliably to specific user instructions. It is well-suited for developers building applications that require a compact, locally-deployable or API-accessible model with a balance of capability and resource efficiency. The training data has a cutoff of March 2023.
What Llama 3 8B Instruct supports
Instruction Following
Responds to explicit user instructions with improved steerability, achieved through post-training refinements that reduced false refusal rates.
Code Generation
Generates and completes code across common programming languages, with code generation listed as a core capability improvement in the Llama 3 release.
Reasoning
Handles multi-step reasoning tasks within an 8,000-token context window, with reasoning cited as a key area of improvement over the prior Llama 2 generation.
Text Generation
Produces natural language text for tasks such as summarization, drafting, and question answering, operating as a decoder-only autoregressive transformer.
Dialogue and Chat
Optimized for conversational use cases through instruction fine-tuning, making it suitable for chatbot and assistant-style application development.
Ready to build with Llama 3 8B Instruct?
Get Started FreeCommon questions about Llama 3 8B Instruct
What is the context window for Llama 3 8B Instruct?
The context window for this model is 8,000 tokens, as specified in the model metadata.
What is the training data cutoff for this model?
The training data cutoff is March 2023, as listed in the model metadata.
Who developed Llama 3 8B Instruct?
Llama 3 8B Instruct was developed by Meta and is part of the Llama 3 model family, which includes 8B and 70B parameter variants.
What is the difference between the base Llama 3 8B model and the Instruct variant?
The Instruct variant has been fine-tuned with instruction-following data during post-training, making it more responsive to explicit user prompts compared to the base pretrained model. It also has reduced false refusal rates and improved alignment.
What types of tasks is Llama 3 8B Instruct best suited for?
According to the model metadata, it is designed for instruction following, code generation, and reasoning tasks. Its 8B parameter size makes it suitable for applications where resource efficiency is a consideration.
What people think about Llama 3 8B Instruct
Community discussions in the LocalLLaMA subreddit frequently include Llama 3 8B Instruct in open-source model benchmarks and hardware performance comparisons, reflecting its continued use as a reference point among locally-run models. Users running models on consumer and prosumer hardware often include it in benchmark suites alongside newer releases.
Some threads highlight that newer models have emerged since Llama 3's release, with community members comparing it against more recent alternatives in benchmark rankings. It continues to appear in practical use cases such as NPC dialogue in game development projects, suggesting ongoing adoption for lightweight, locally-deployed applications.
I locally benchmarked 41 open-source LLMs across 19 tasks and ranked them
AMD Instinct MI60 (32gb VRAM) "llama bench" results for 10 models - Qwen3 30B A3B Q4_0 resulted in: pp512 - 1,165 t/s | tg128 68 t/s - Overall very pleased and resulted in a better outcome for my use case than I even expected
Obsessed with Claude Code here.
How am I building a hacking sim game themed on 90s with NPCs powered by AI (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 8B Instruct
No API keys required. Create AI-powered workflows with Llama 3 8B Instruct in minutes — free.