Nemotron Labs Diffusion 8B — Hardware Requirements & GPU Compatibility
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Nemotron Labs Diffusion 8B is a 8.5B-parameter open language model from NVIDIA. It supports a context window of up to 262,144 tokens. At BF16 it needs about 17.56 GB of VRAM — see which GPUs and Macs can run it below.
68.8K downloads
47 likesSpecifications
- Publisher
- NVIDIA
- Parameters
- 8.5B
- Architecture
- NemotronLabsDiffusionModel
- Context Length
- 262,144 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-06-03
- License
- Other
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HuggingFace
How Much VRAM Does Nemotron Labs Diffusion 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 17.6 GB | 53.8 GB | 16.98 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Nemotron Labs Diffusion 8B?
BF16 · 17.6 GBNemotron Labs Diffusion 8B (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 262K context window can add up to 36.2 GB, bringing total usage to 53.8 GB. 6 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?
BF16 · 17.6 GB21 devices with unified memory can run Nemotron Labs Diffusion 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Nemotron Labs Diffusion 8B need?
Nemotron Labs Diffusion 8B requires 17.6 GB of VRAM at BF16. Full 262K context adds up to 36.2 GB (53.8 GB total).
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 ≈ 36.8 GB (at full 262K context)
VRAM usage by quantization
BF1617.6 GBBF16 + full context53.8 GB- Can I run Nemotron Labs Diffusion 8B on a Mac?
Nemotron Labs Diffusion 8B 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 locally?
Yes — Nemotron Labs Diffusion 8B 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?
At BF16, Nemotron Labs Diffusion 8B can reach ~166 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 17.6 × 0.55 = ~166 tok/s
Estimated speed at BF16 (17.6 GB)
~166 tok/s~37 tok/s~124 tok/s~103 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?
At BF16, the download is about 16.98 GB.
- Which GPUs can run Nemotron Labs Diffusion 8B?
6 consumer GPUs can run Nemotron Labs Diffusion 8B 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?
21 devices with unified memory can run Nemotron Labs Diffusion 8B at BF16 (17.6 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.