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.
Discover the power and diversity of large language models available with Telnyx. Explore the options below to find the perfect model for your project.
| Organization | Model Name | Tasks | Languages Supported | Context Length | Parameters | Model Tier | License |
|---|---|---|---|---|---|---|---|
| No data available at this time, please try again later. |
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 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.
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.
