nvidia / text-generation updated 9 months ago

Llama-3_3-Nemotron-Super-49B-v1_5-FP8

Llama-3.3-Nemotron-Super-49B-v1.5-FP8 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as RAG and tool calling. The model supports a context length of 128K tokens.

Params
49.9 B
Context
131,072
Downloads 30d
256 K
Likes
28
Licence: other Not gated SAFETENSORS 1 languages View on Hugging Face ↗

Download history

daily snapshots · 10 days
256 K250 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 52.0 GB 65.1 GB ❌ Won’t fit
model.safetensors (bf16, full) bf16 + 128K ctx 52.0 GB 177.4 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/llama-3-3-nemotron-super-49b-v1-5-fp8
{
  "hf_id": "nvidia/Llama-3_3-Nemotron-Super-49B-v1_5-FP8",
  "params_b": 49.87,
  "context_length": 131072,
  "license": { "id": "other", "commercial": "unknown" },
  "downloads_30d": 255770,
  "vram_estimates": [
    { "quant": "f8_e4m3", "gb": 65.1 }
  ],
  "updated_at": "2026-07-28T18:07:09Z"
}
est. VRAM —on RTX 4090 · 24 GBJSON API →

Specifications

Architecture
DeciLMForCausalLM
Parameters
49.9 B
Tensor type
F8_E4M3
Context length
131,072
Vocabulary
128,256
Layers / heads
80 / 64
Licence
other
First seen on the Hub
2025-07-31
Training datasets
undisclosed
Added to our catalog
2026-07-28