Llama 3.1 8B Instruct
The workhorse 8B instruction-tuned model. Excellent quality-to-cost ratio and the broadest ecosystem support of any open-weights model — every major inference engine, fine-tuning library, and quantization toolchain has a 3.1 8B preset. Fits in 24 GB of VRAM at fp16, ~6 GB at Q4. Strong default for production chat where 70B is overkill, for fine-tuning on a specialist task, and for any workload where you want a known-good baseline.
- Parameters
- 8B
- Context length
- 128K
- Modality
- text
- Released
- 2024-07-23
Memory & hardware
- VRAM (fp16)
- 16 GB
- VRAM (Q4)
- 4.8 GB
- Recommended
- RTX 3090 24GB
- Quantizations
- fp16, fp8, q8_0, q5_k_m, q4_k_m, gguf
License: Llama 3 Community License
- SPDX
- —
- Commercial use
- Yes
- Modification
- Yes
- Redistribution
- Yes
Benchmarks
Hosted inference pricing
USD per million tokens.
| Provider | Input | Output | |
|---|---|---|---|
| groqCheapest | $0.05 | $0.08 | |
| together | $0.18 | $0.18 |
Run it yourself
Drop-in commands for the three most common open-source inference paths. The Ollama tag is a best-effort match against the registry; verify the size variant before pulling.
ollama run llama3.1:8b
vllm serve meta-llama/Llama-3.1-8B-Instruct
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B-Instruct", device_map="auto", torch_dtype="auto"
)meta-llama/Llama-3.1-8B-Instruct Related models
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