Nemotron Labs Diffusion 14B — Hardware Requirements & GPU Compatibility
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Nemotron Labs Diffusion 14B is a 13.5B-parameter open language model from NVIDIA. It supports a context window of up to 262,144 tokens. At BF16 it needs about 27.73 GB of VRAM — see which GPUs and Macs can run it below.
12.5K downloads
139 likesSpecifications
- Publisher
- NVIDIA
- Parameters
- 13.5B
- Architecture
- NemotronLabsDiffusionModel
- Context Length
- 262,144 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-06-03
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Nemotron Labs Diffusion 14B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 27.7 GB | 81 GB | 27.01 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Nemotron Labs Diffusion 14B?
BF16 · 27.7 GBNemotron Labs Diffusion 14B (BF16) requires 27.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 37+ GB is recommended. Using the full 262K context window can add up to 53.3 GB, bringing total usage to 81 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Nemotron Labs Diffusion 14B?
BF16 · 27.7 GB15 devices with unified memory can run Nemotron Labs Diffusion 14B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Nemotron Labs Diffusion 14B need?
Nemotron Labs Diffusion 14B requires 27.7 GB of VRAM at BF16. Full 262K context adds up to 53.3 GB (81 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 13.5B × 16 bits ÷ 8 = 27 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 54 GB (at full 262K context)
VRAM usage by quantization
BF1627.7 GBBF16 + full context81.0 GB- Can I run Nemotron Labs Diffusion 14B on a Mac?
Nemotron Labs Diffusion 14B requires at least 27.7 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 14B locally?
Yes — Nemotron Labs Diffusion 14B can run locally on consumer hardware. At BF16 quantization it needs 27.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Nemotron Labs Diffusion 14B?
At BF16, Nemotron Labs Diffusion 14B can reach ~105 tok/s on AMD Instinct MI300X. 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 ÷ 27.7 × 0.55 = ~105 tok/s
Estimated speed at BF16 (27.7 GB)
~105 tok/s~79 tok/s~65 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 14B?
At BF16, the download is about 27.01 GB.
- Which GPUs can run Nemotron Labs Diffusion 14B?
1 consumer GPU can run Nemotron Labs Diffusion 14B at BF16 (27.7 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Nemotron Labs Diffusion 14B?
15 devices with unified memory can run Nemotron Labs Diffusion 14B at BF16 (27.7 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.