RedHatAI / text-generation updated 1 year ago

Meta-Llama-3.1-8B-FP8

- Model Architecture: Meta-Llama-3.1 - Input: Text - Output: Text - Model Optimizations: - Weight quantization: FP8 - Activation quantization: FP8 - Intended Use Cases: Intended for commercial and research use in multiple languages. Similarly to Meta-Llama-3.1-8B, this model serves as a base version. - Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use...

Params
8.0 B
Context
131,072
Downloads 30d
230 K
Likes
10
Commercial use: conditional · llama3.1 Not gated SAFETENSORS 8 languages View on Hugging Face ↗

Download history

daily snapshots · 10 days
231 K221 K
Jul 28Jul 31Aug 3Aug 6

Can you run it?

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

FileQuantSizeEst. VRAMVerdict on RTX 4090 · 24 GB
model.safetensors f8_e4m3 9.1 GB 11.7 GB ✅ Runs comfortably
model.safetensors (bf16, full) bf16 + 128K ctx 9.1 GB 29.8 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-fp8
{
  "hf_id": "RedHatAI/Meta-Llama-3.1-8B-FP8",
  "params_b": 8.03,
  "context_length": 131072,
  "license": { "id": "llama3.1", "commercial": "conditional" },
  "downloads_30d": 229809,
  "vram_estimates": [
    { "quant": "f8_e4m3", "gb": 11.7 }
  ],
  "updated_at": "2026-07-28T18:07:35Z"
}
est. VRAM —on RTX 4090 · 24 GBJSON API →

Specifications

Architecture
LlamaForCausalLM
Parameters
8.0 B
Tensor type
F8_E4M3
Context length
131,072
Vocabulary
128,256
Layers / heads
32 / 32
Licence
llama3.1
First seen on the Hub
2024-07-31
Training datasets
undisclosed
Added to our catalog
2026-07-28