What is Llama-3.1-8B-Instruct?
Llama-3.1-8B-Instruct is the smallest of the three models in Meta's Llama 3.1 collection (8B, 70B and 405B): an 8 billion parameter, instruction tuned, text only model optimized for multilingual dialogue. Meta aligned it with supervised fine-tuning and reinforcement learning with human feedback and released it on July 23, 2024. The size is what makes it a local staple: the Q4_K_M build is a 4.92 GB file, so the model fits an 8 GB GPU with room left for context.
| Specification | Llama-3.1-8B-Instruct |
|---|---|
| Parameters | 8B (8,030,261,248 in the BF16 weights) |
| Architecture | Auto-regressive, optimized transformer |
| Attention | Grouped-Query Attention (GQA) |
| Context window | 128K tokens |
| Modalities | Multilingual text in, multilingual text and code out |
| Supported languages | English, German, French, Italian, Portuguese, Hindi, Spanish and Thai |
| Pretraining data | Publicly available online data, 15T+ tokens |
| Knowledge cutoff | December 2023 |
| Alignment | Supervised fine-tuning and RLHF |
| Fine-tuning data | Public instruction datasets plus over 25M synthetic examples |
| Training compute | 1.46M GPU hours on H100-80GB |
| Release date | July 23, 2024 |
| License | Llama 3.1 Community License |
Meta ships the repository in two versions, one for Transformers 4.43.0 and later and one for the original llama codebase, and points at the huggingface-llama-recipes collection for local runs with torch.compile(), assisted generation and quantised weights. Llama 3.1 also supports multiple tool use formats, and tool calling is wired into the Transformers chat template: you pass Python functions to apply_chat_template and append each result back with the tool role. That is enough to drive a small agent loop on your own machine. Meta calls this a static model trained on an offline dataset.
Llama-3.1-8B-Instruct benchmarks
Meta's published numbers, from the model card, compare the instruction tuned 8B with its predecessor Llama 3 8B Instruct and the two larger Llama 3.1 models:
| Benchmark | Llama 3.1 8B Instruct | Llama 3 8B Instruct | Llama 3.1 70B Instruct | Llama 3.1 405B Instruct |
|---|---|---|---|---|
MMLU General knowledge | 69.4 | 68.5 | 83.6 | 87.3 |
IFEval Instruction following | 80.4 | 76.8 | 87.5 | 88.6 |
GPQA Expert science | 30.4 | 34.6 | 46.7 | 50.7 |
HumanEval Code generation | 72.6 | 60.4 | 80.5 | 89.0 |
MATH (CoT) Harder math | 51.9 | 29.1 | 68.0 | 73.8 |
API-Bank Tool APIs | 82.6 | 48.3 | 90.0 | 92.0 |
BFCL Function calling | 76.1 | 60.3 | 84.8 | 88.5 |
Multilingual MGSM Multilingual math | 68.9 | - | 86.9 | 91.6 |
Inside the family the 405B takes every row, as you would expect. The column worth reading is Llama 3 8B Instruct: at the same size, the 3.1 update gains 34.3 points on API-Bank, 22.8 on MATH, 15.8 on BFCL and 12.2 on HumanEval, and gives back 4.2 points on GPQA.
Meta's own framing is narrow. The instruction tuned text only models are built for assistant-like chat in the eight supported languages, and Meta claims they "outperform many of the available open source and closed chat models" on common industry benchmarks. The multilingual half of that claim comes with its own table: on per-language MMLU the 8B scores 62.45 in Spanish, 62.34 in French, 62.12 in Portuguese, 61.63 in Italian and 60.59 in German, while Hindi lands at 50.88 and Thai at 50.32, about ten points below the European languages. Meta notes the model saw more languages in pretraining than the eight it supports, and discourages conversing in the others without fine-tuning and system controls of your own.
Llama-3.1-8B-Instruct hardware requirements
The system requirement to check is memory. The builds below, with their real file sizes, come from unsloth/Llama-3.1-8B-Instruct-GGUF.
| Memory | Build to pick | File size |
|---|---|---|
| 4 GB | UD-Q2_K_XL | 3.39 GB |
| 6 GB | Q3_K_M | 4.02 GB |
| 8 GB | Q4_K_M | 4.92 GB |
| 10 GB | Q5_K_M | 5.73 GB |
| 12 GB | Q6_K | 6.60 GB |
| 16 GB | UD-Q8_K_XL | 10.58 GB |
| 24 GB and up | BF16 | 16.07 GB |
Neighbouring builds are often only a few hundred megabytes apart, so when two both fit, take the larger one. That matters most at the bottom of the table, where quality falls fastest: the repo goes down to 2.16 GB with UD-IQ1_S, but files that small are a last resort, not a default. Leave some headroom if you plan to use long inputs, the 128K window needs memory of its own. If the format is new to you, start with what GGUF is, and see the best local LLMs for a 16 GB Mac for what else fits alongside it.
How to run Llama-3.1-8B-Instruct in Atomic Chat
Atomic Chat is a free local app for macOS, Windows and Linux. It includes a Hugging Face model browser and a built-in chat, with no manual llama.cpp build required.
- Download Atomic Chat for your platform and open it.
- Search for Llama-3.1-8B-Instruct in the model browser and open Download Options.
- Pick the build that fits the memory you have, then start a chat.
For the rest of the family, see every Llama model you can run locally, or the smaller sibling Llama-3.2-3B-Instruct.
Llama-3.1-8B-Instruct license
Llama-3.1-8B-Instruct ships under the Llama 3.1 Community License, a custom commercial license from Meta. It permits commercial and research use in the supported languages, and it explicitly allows using the model's outputs to improve other models, including synthetic data generation and distillation. Use has to stay inside applicable law, Meta's Acceptable Use Policy and the eight supported languages, so read the license text before shipping a product on top of it.