Llama 3.1 8B
Q4 GGUF download: bartowski/Meta-Llama-3.1-8B-Instruct-GGUF - 4.92 GB on disk, read 2026-09-27. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
6.9
AA Index
Coding
5.4
AA Index
Intelligence Index - Llama 3.1 8B vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Coding Index comparison
Benchmark data from Artificial Analysis · updated 2026-10-19.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.04 |
| Output (per 1M tokens) | $0.07 |
| Median speed (hosted API) | 141 tok/s |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-19. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| MMLU | 69.4 |
| HumanEval | 72.1 |
| MT-Bench | 8.1 |
| GSM8K | 84.5 |
Other scores are carried over from the site's initial dataset - no source on record, not re-verified.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
ollama run llama3.1:8bvllm serve meta-llama/Meta-Llama-3.1-8BNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Llama 3.1 8B
Llama 3.1 8B is Meta's landmark 8B model that set the standard for open-weight LLMs. With 128K context, Grouped-Query Attention (GQA), and a permissive license, it remains a popular choice in 2026.
Key features
- 128K context - handles extremely long documents
- GQA - Grouped-Query Attention for efficient inference
- Extensive ecosystem - thousands of fine-tunes (Hermes, Dolphin, etc.)
- 10.4M+ monthly downloads on HuggingFace
VRAM math
| Quant | VRAM | Recommended Hardware |
|---|---|---|
| Q4_K_M | ~5.5 GB | RTX 3060, Apple Silicon |
| Q5_K_M | ~7 GB | RTX 3090 |
| Q8_0 | ~10 GB | RTX 4090 |
| FP16 | ~16 GB | RTX 4090 |
How to run
ollama run llama3.1:8bFrom the community
What users are saying
“Hello, I am a student learning AI and machine learning, and I would like to request access to this model for educational and research purposes. I am using it to learn how to integrate Hugging Face models into my Python projects and to understand how large language models work.”
“How do I get the models? You can get the Meta Llama models directly from Meta or through Hugging Face or Kaggle. However you get the models, you will first need to accept the license agreements for the models you want.”
“Dear Meta Llama Team, My request to access meta-llama/Llama-3.1-8B was rejected. I would like to respectfully ask whether my access request can be re-evaluated. I am using the model for academic research on large language models at Zhejiang University.”
Frequently asked
Quick answers to common questions
How much VRAM does Llama 3.1 8B need?
Llama 3.1 8B with 8B parameters needs approximately 5.5 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Llama 3.1 8B better than other Llama models?
Llama 3.1 8B scores 69.4 on MMLU and 72.1 on HumanEval. It has 8B parameters with 131,072 context - a strong choice for general-purpose, chatbots, coding.
What license is Llama 3.1 8B under?
Llama 3.1 8B is released under the Llama 3.1 Community License license, making it suitable for most commercial and personal projects.
What hardware runs Llama 3.1 8B well?
With 8B parameters, Llama 3.1 8B requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Llama 3.1 8B?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~7 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can Llama 3.1 8B's context window handle?
Llama 3.1 8B supports a 131,072-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with Llama 3.1 8B?
Llama 3.1 8B competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Compare & pair with
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