Model ID
meta-llama/Llama-3.1-8B-Instruct
Capabilities
Llama 3.1 8B Instruct
Llama 3.1 8B Instruct is an instruction-tuned dense transformer large language model optimized for multilingual conversational AI and assistant-style applications. Built on an 8B parameter architecture, it features 32 transformer layers, 32 attention heads, and a 4,096 hidden size, leveraging Grouped Query Attention (GQA) for efficient inference. The model supports up to a 128K token context window with advanced RoPE scaling for long-context processing. Fine-tuned using supervised learning and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
About this model
Llama 3.1 8B Instruct is an instruction-tuned dense transformer large language model optimized for multilingual conversational AI and assistant-style applications. Built on an 8B parameter architecture, it features 32 transformer layers, 32 attention heads, and a 4,096 hidden size, leveraging Grouped Query Attention (GQA) for efficient inference. The model supports up to a 128K token context window with advanced RoPE scaling for long-context processing. Fine-tuned using supervised learning and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (meta-llama/Llama-3.1-8B-Instruct). Released under Llama3.1. Further reading: Blog
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Hardware
MI325X
2,048 GB
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on MI325X · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
AMD Instinct MI325X
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