Nemotron Labs Diffusion 8B Base — Hardware Requirements & GPU Compatibility
Chat
Nemotron Labs Diffusion 8B Base is a 8.5B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 4,096 tokens. At BF16 it needs about 17.56 GB of VRAM — see which GPUs and Macs can run it below.
86.5K downloads
8 likesSpecifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 8.5B
- Architecture
- NemotronLabsDiffusionModel
- Context Length
- 4,096 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-01-14
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Nemotron Labs Diffusion 8B Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 17.6 GB | 17.9 GB | 16.98 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 Nemotron Labs Diffusion 8B Base?
BF16 · 17.6 GBNemotron Labs Diffusion 8B Base (BF16) requires 17.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 4K context window can add up to 0.3 GB, bringing total usage to 17.9 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Nemotron Labs Diffusion 8B Base?
BF16 · 17.6 GB41 devices with unified memory can run Nemotron Labs Diffusion 8B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Nemotron Labs Diffusion 8B Base need?
Nemotron Labs Diffusion 8B Base requires 17.6 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.5B × 16 bits ÷ 8 = 17 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.9 GB (at full 4K context)
VRAM usage by quantization
BF1617.6 GBBF16 + full context17.9 GB- Can I run Nemotron Labs Diffusion 8B Base on a Mac?
Nemotron Labs Diffusion 8B Base requires at least 17.6 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 Nemotron Labs Diffusion 8B Base locally?
Yes — Nemotron Labs Diffusion 8B Base can run locally on consumer hardware. At BF16 quantization it needs 17.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Nemotron Labs Diffusion 8B Base?
At BF16, Nemotron Labs Diffusion 8B Base can reach ~273 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~37 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 ÷ 17.6 × 0.65 = ~296 tok/s
Estimated speed at BF16 (17.6 GB)
~296 tok/s~37 tok/s~296 tok/s~273 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Nemotron Labs Diffusion 8B Base?
At BF16, the download is about 16.98 GB.
- Which GPUs can run Nemotron Labs Diffusion 8B Base?
8 consumer GPUs can run Nemotron Labs Diffusion 8B Base at BF16 (17.6 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Nemotron Labs Diffusion 8B Base?
41 devices with unified memory can run Nemotron Labs Diffusion 8B Base at BF16 (17.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.