Qwen3-Next-80B-A3B-Instruct-FP8
Over the past few months, we have observed increasingly clear trends toward scaling both total parameters and context lengths in the pursuit of more powerful and agentic artificial intelligence (AI). We are excited to share our latest advancements in addressing these demands, centered on improving scaling efficiency through innovative model architecture. We call this next-generation foundation models Qwen3-Next.
Params
81.3 B
Context
262,144
Downloads 30d
185 K
Likes
90
Download history
daily snapshots · 106 days
▲ 0 in the last 30 days (0.0%)
185 K185 K
Oct 12Oct 22Nov 1Nov 10
300 K185 K
Aug 13Sep 12Oct 12Nov 10
339 K185 K
Jul 28Sep 1Oct 6Nov 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 | 82.1 GB | 103.0 GB | ❌ Won’t fit |
| model.safetensors (bf16, full) | bf16 + 256K ctx | 82.1 GB | 481.1 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/qwen3-next-80b-a3b-instruct-fp8
{
"hf_id": "Qwen/Qwen3-Next-80B-A3B-Instruct-FP8",
"params_b": 81.33,
"context_length": 262144,
"license": { "id": "apache-2.0", "commercial": "yes" },
"downloads_30d": 185311,
"vram_estimates": [
{ "quant": "f8_e4m3", "gb": 103.0 }
],
"updated_at": "2026-07-28T18:06:17Z"
}
Specifications
- Architecture
- Qwen3NextForCausalLM
- Parameters
- 81.3 B
- Tensor type
- F8_E4M3
- Context length
- 262,144
- Vocabulary
- 151,936
- Layers / heads
- 48 / 16
- Licence
- apache-2.0
- First seen on the Hub
- 2025-09-22
- Base model
- Qwen3-Next-80B-A3B-Instruct
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-28
Family
Base model and the most-downloaded derivatives in the catalog.
Compare with any text-generation model