gpt-oss-120b-Eagle3-short-context
The NVIDIA gpt-oss-120b Eagle model is the Eagle head of the OpenAI’s gpt-oss-120b model, which is an auto-regressive language model that uses a mixture-of-experts (MoE) architecture with 5 billion activated parameters and 120 billion total parameters. For more information, please check here. The NVIDIA gpt-oss-120b Eagle3 model incorporates Eagle speculative decoding with Model Optimizer.
Params
787 M
Context
131,072
Downloads 30d
141 K
Likes
22
Download history
tracking started — chart appears after 7 days of snapshots (1 recorded)141 K downloads in the last 30 days
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 | 2.0 GB | 2.8 GB | ✅ Runs comfortably |
| model.safetensors (bf16, full) | bf16 + 128K ctx | 2.0 GB | 4.6 GB | ✅ Runs comfortably |
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/gpt-oss-120b-eagle3-short-context
{
"hf_id": "nvidia/gpt-oss-120b-Eagle3-short-context",
"params_b": 0.79,
"context_length": 131072,
"license": { "id": "other", "commercial": "unknown" },
"downloads_30d": 141204,
"vram_estimates": [
{ "quant": "bf16", "gb": 2.8 }
],
"updated_at": "2026-10-25T01:00:32Z"
}
Specifications
- Architecture
- LlamaForCausalLMEagle3
- Parameters
- 787 M
- Tensor type
- BF16
- Context length
- 131,072
- Vocabulary
- 201,088
- Layers / heads
- 1 / 64
- Licence
- other
- First seen on the Hub
- 2025-10-06
- Base model
- gpt-oss-120b
- Training datasets
- undisclosed
- Added to our catalog
- 2026-10-25
Family
Base model and the most-downloaded derivatives in the catalog.
Compare with any text-generation model