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
No provider we track publishes a per-token price for this model today. What each one used to offer is listed below.
No longer listed
Providers that used to serve this model. We don't republish their old rates — the dates below are the provider's own.
- groqGroq withdrew public per-token pricing for llama-3.1-8b-instant on 16 August 2026, for free and developer tiers. It remains available to enterprise customers on request, with no published rate.Source ↗
- togetherNot in Together AI’s serverless catalogue when we checked on 20 September 2026.Source ↗
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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