NVIDIA Nemotron Parse 2.0 — Hardware Requirements & GPU Compatibility
Vision
NVIDIA Nemotron Parse 2.0 is a sub-1-billion-parameter vision-encoder-decoder model purpose-built for document parsing rather than open-ended chat: given a page image, it outputs structured text with layout classes, bounding boxes, and reading order for elements like titles, paragraphs, tables, charts, and footnotes. It pairs a ViT-H vision encoder based on NVIDIA's C-RADIO with a 10-block mBART decoder, and over its predecessor v1.2 it adds roughly 20,000 new vocabulary tokens for more efficient multilingual (especially CJK and Indic-script) OCR, a dedicated chart class for chart-to-table parsing, and stronger table detection and text extraction. At under a billion parameters it runs on a single modest GPU. License is the OpenMDW License Agreement version 1.1, permitting both commercial and non-commercial use; the bundled tokenizer is separately licensed under CC-BY-4.0. It was published in August 2026.
102.4K downloads
116 likesSpecifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 903M
- Architecture
- NemotronParseForConditionalGeneration
- Vocabulary Size
- 72,256
- Release Date
- 2026-06-30
- License
- openmdw-1.1
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HuggingFace
How Much VRAM Does NVIDIA Nemotron Parse 2.0 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 2.0 GB | — | 1.81 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run NVIDIA Nemotron Parse 2.0?
BF16 · 2.0 GBNVIDIA Nemotron Parse 2.0 (BF16) requires 2.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run NVIDIA Nemotron Parse 2.0?
BF16 · 2.0 GB59 devices with unified memory can run NVIDIA Nemotron Parse 2.0, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does NVIDIA Nemotron Parse 2.0 need?
NVIDIA Nemotron Parse 2.0 requires 2.0 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 903M × 16 bits ÷ 8 = 1.8 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF162.0 GB- Can I run NVIDIA Nemotron Parse 2.0 on a Mac?
NVIDIA Nemotron Parse 2.0 requires at least 2.0 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run NVIDIA Nemotron Parse 2.0 locally?
Yes — NVIDIA Nemotron Parse 2.0 can run locally on consumer hardware. At BF16 quantization it needs 2.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is NVIDIA Nemotron Parse 2.0?
At BF16, NVIDIA Nemotron Parse 2.0 can reach ~2412 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~329 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.0 × 0.65 = ~2613 tok/s
Estimated speed at BF16 (2.0 GB)
~2613 tok/s~329 tok/s~2613 tok/s~2412 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of NVIDIA Nemotron Parse 2.0?
At BF16, the download is about 1.81 GB.
- Which GPUs can run NVIDIA Nemotron Parse 2.0?
52 consumer GPUs can run NVIDIA Nemotron Parse 2.0 at BF16 (2.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run NVIDIA Nemotron Parse 2.0?
59 devices with unified memory can run NVIDIA Nemotron Parse 2.0 at BF16 (2.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.