NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
--- libraryname: transformers license: other licensename: openmdw-1.1 licenselink: https://openmdw.ai/license/1-1/ pipelinetag: text-generation language: - en - es - fr - de - it - ja tags: - nvidia - pytorch - nemotron-3.5 datasets: - nvidia/nemotron-post-training-v3 - nvidia/nemotron-pre-training-datasets trackdownloads: true ---
Params
31.6 B
Context
262,144
Downloads 30d
570 K
Likes
218
Download history
daily snapshots · 26 days582 K376 K
Aug 27Sep 4Sep 13Sep 21
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 | 65.8 GB | 77.6 GB | ❌ Won’t fit |
| model.safetensors (bf16, full) | bf16 + 256K ctx | 65.8 GB | 224.5 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/nvidia-nemotron-3-5-lightning-30b-a3b-bf16
{
"hf_id": "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
"params_b": 31.58,
"context_length": 262144,
"license": { "id": "other", "commercial": "unknown" },
"downloads_30d": 569990,
"vram_estimates": [
{ "quant": "bf16", "gb": 77.6 }
],
"updated_at": "2026-08-27T01:00:14Z"
}
Specifications
- Architecture
- NemotronHForCausalLM
- Parameters
- 31.6 B
- Tensor type
- BF16
- Context length
- 262,144
- Vocabulary
- 131,072
- Layers / heads
- 52 / 32
- Licence
- other
- First seen on the Hub
- 2026-08-01
- Training datasets
- nvidia/nemotron-post-training-v3, nvidia/nemotron-pre-training-datasets
- Added to our catalog
- 2026-08-27
Family
Base model and the most-downloaded derivatives in the catalog.
Compare with any text-generation model