unsloth / text-generation updated 1 year ago

Meta-Llama-3.1-8B-Instruct

Name: Meta-Llama-3.1-8B-Instruct
Author: unsloth

We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing

Params

8.0 B

Context

131,072

Downloads 30d

260 K

Likes

Commercial use: conditional · llama3.1 Not gated SAFETENSORS 1 languages View on Hugging Face ↗

Download history

daily snapshots · 55 days

▲ 134 K in the last 30 days (34.1%)

410 K260 K

Jul 28Aug 15Sep 2Sep 20

Can you run it?

Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.

File	Quant	Size	Est. VRAM	Verdict on RTX 4090 · 24 GB
model.safetensors	bf16	16.1 GB	19.4 GB	✅ Runs comfortably
model.safetensors (bf16, full)	bf16 + 128K ctx	16.1 GB	37.4 GB	❌ Won’t fit

Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.

Run it

copy-paste, exact tags checked against the Hub

~ · curl · api/v1

$ curl -s https://aimodelscomparison.com/api/v1/models/meta-llama-3-1-8b-instruct-unsloth
{
  "hf_id": "unsloth/Meta-Llama-3.1-8B-Instruct",
  "params_b": 8.03,
  "context_length": 131072,
  "license": { "id": "llama3.1", "commercial": "conditional" },
  "downloads_30d": 259559,
  "vram_estimates": [
    { "quant": "bf16", "gb": 19.4 }
  ],
  "updated_at": "2026-07-28T18:06:08Z"
}

est. VRAM —on RTX 4090 · 24 GBJSON API →

Specifications

Architecture: LlamaForCausalLM
Parameters: 8.0 B
Tensor type: BF16
Context length: 131,072
Vocabulary: 128,256
Layers / heads: 32 / 32
Licence: llama3.1
First seen on the Hub: 2024-07-23
Base model: Llama-3.1-8B-Instruct
Training datasets: undisclosed
Added to our catalog: 2026-07-28

Family

Base model and the most-downloaded derivatives in the catalog.

Base Llama-3.1-8B-Instruct meta-llama 5.9 M

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