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Jumping from 8K to 128K tokens of context versus Llama 3, this model was fine-tuned on 25 million synthetic examples generated from the larger 405B variant and aligned using a combination of rejection sampling and Direct Preference Optimization. It was the first open-weight 8B model to ship with native tool-calling support across 8 languages, trained on over 15 trillion tokens of public data.
Llama 3.1 8B Instruct scores 69.4% on MMLU (5-shot) and 73.0% on MMLU (0-shot CoT), improving over Llama 3 8B Instruct (67.4% on 5-shot) by about 2 points on the same configuration. It also scores 72.6% on HumanEval, more than double the scores of Mistral 7B v0.2 (30.5%) and Gemma 7B IT (32.3%) on the same sheet.
The cost of running Llama 3.1 8B Instruct with Telnyx Inference is $0.0002 per 1,000 tokens. Analyzing 1,000,000 customer chats at 1,000 tokens each would cost $200, the same as Llama 3 8B Instruct but with stronger benchmark performance across the board.
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Llama 3.1 8B Instruct is well suited for conversational AI, code generation, and text summarization tasks where a balance of capability and efficiency is needed. Its compact size makes it a practical choice for production inference deployments that require low latency and manageable compute costs.
Llama 3.1 8B Instruct is Meta's instruction-tuned 8 billion parameter model from the Llama 3.1 family, released in July 2024. It supports a 128K context window and multiple languages, with strong performance on reasoning and code tasks relative to its size.
Llama 3.1 8B requires approximately 16GB of VRAM for full-precision inference, or 8GB when using 4-bit quantization. Alternatively, hosted inference platforms like Telnyx provide API access without managing local GPU infrastructure.
Llama 3.1 8B does not match GPT-4's performance on complex reasoning and multi-step tasks, as GPT-4 is a significantly larger model. However, for straightforward generation and code assistance tasks, the 8B model offers competitive results at a fraction of the cost.
An NVIDIA GPU with at least 8GB of VRAM (such as an RTX 3070 or above) can run Llama 3.1 8B using quantized formats. For full-precision inference, 16GB+ GPUs like the RTX 4090 or A100 are recommended.
