Model ID
meta-llama/Llama-3.1-70B-Instruct
Capabilities
Llama 3.1 70B Instruct
Llama 3.1 70B Instruct is an instruction-tuned dense transformer large language model optimized for multilingual dialogue, reasoning, and assistant-style AI applications. Built on a 70B parameter architecture, it features 80 transformer layers, 64 attention heads, and an 8,192 hidden size, leveraging Grouped Query Attention (GQA) for efficient large-scale inference. The model supports up to a 128K token context window with RoPE scaling for long-context understanding. 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 70B Instruct is an instruction-tuned dense transformer large language model optimized for multilingual dialogue, reasoning, and assistant-style AI applications. Built on a 70B parameter architecture, it features 80 transformer layers, 64 attention heads, and an 8,192 hidden size, leveraging Grouped Query Attention (GQA) for efficient large-scale inference. The model supports up to a 128K token context window with RoPE scaling for long-context understanding. 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-3.1-70B-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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