Meta-Llama-3.1-70B-Instruct-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-Instruct, this models is intended for assistant-like chat. - Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade c...
Params
70.6 B
Context
131,072
Downloads 30d
142 K
Likes
52
Download history
daily snapshots · 11 days142 K139 K
Oct 31Nov 3Nov 7Nov 10
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 | f8_e4m3 | 72.7 GB | 91.0 GB | ❌ Won’t fit |
| model.safetensors (bf16, full) | bf16 + 128K ctx | 72.7 GB | 249.8 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 -s https://aimodelscomparison.com/api/v1/models/meta-llama-3-1-70b-instruct-fp8
{
"hf_id": "RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8",
"params_b": 70.55,
"context_length": 131072,
"license": { "id": "llama3.1", "commercial": "conditional" },
"downloads_30d": 141993,
"vram_estimates": [
{ "quant": "f8_e4m3", "gb": 91.0 }
],
"updated_at": "2026-10-31T01:00:22Z"
}
Specifications
- Architecture
- LlamaForCausalLM
- Parameters
- 70.6 B
- Tensor type
- F8_E4M3
- Context length
- 131,072
- Vocabulary
- 128,256
- Layers / heads
- 80 / 64
- Licence
- llama3.1
- First seen on the Hub
- 2024-07-23
- Training datasets
- undisclosed
- Added to our catalog
- 2026-10-31
Compare with any text-generation model