Model ID
meta-llama/Llama-3.1-70B
Capabilities
Llama 3.1 70B
Llama 3.1 70B is a multilingual dense transformer large language model designed for advanced text generation, reasoning, and large-scale AI applications. The model features a 70B parameter architecture with 80 transformer layers, 64 attention heads, and an 8,192 hidden size, utilizing Grouped Query Attention (GQA) for scalable and efficient inference. It supports up to a 128K token context window with RoPE scaling for long-context understanding. Optimized for multilingual text and code generation, it is widely used for research, conversational AI, and enterprise-grade LLM deployments.
About this model
Llama 3.1 70B is a multilingual dense transformer large language model designed for advanced text generation, reasoning, and large-scale AI applications. The model features a 70B parameter architecture with 80 transformer layers, 64 attention heads, and an 8,192 hidden size, utilizing Grouped Query Attention (GQA) for scalable and efficient inference. It supports up to a 128K token context window with RoPE scaling for long-context understanding. Optimized for multilingual text and code generation, it is widely used for research, conversational AI, and enterprise-grade LLM deployments.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (meta-llama/Llama-3.1-70B). 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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