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 in the Nemotron family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 8.82 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~74 tok/s · 64K)Macs from 16 GB (Q4_K_M · ~16 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Nemotron Labs Diffusion 14B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve nvidia/Nemotron-Labs-Diffusion-14B --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 5060 Ti 16GB (4 GB headroom) · Q4_K_M
- Generation speed
- ~33 tok/s
- generation speed
- Cost per 1M output tokens
- $1.38
- per 1M output tokens
How Much VRAM Does Nemotron Labs Diffusion 14B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 6.5 GB | 12.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 64K context · ~101 tok/s | |
| 7.3 GB | 13.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 64K context · ~90 tok/s | |
| 8.8 GB | 15.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~74 tok/s | |
| 10.3 GB | 16.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~63 tok/s | |
| 11.9 GB | 18.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~55 tok/s | |
| 14.2 GB | 20.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~46 tok/s | |
| 27.7 GB | 34 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 30.7 GB |
Selected Q4_K_M
4-bit medium quantization11.8GBat 16K context
- Weights
- 8.1 GB
- KV cache
- 3.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 12.2 GB free · ~74 tok/s
≥50 tok/s needs ≥678 GB/s · RTX 5090 ~132, RTX 3090 Ti ~74
Which Hardware Can Run Nemotron Labs Diffusion 14B?
Q4_K_M · 8.8 GBNemotron Labs Diffusion 14B (Q4_K_M) requires 8.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. Using the full 262K context window can add up to 53.3 GB, bringing total usage to 62.1 GB. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Nemotron Labs Diffusion 14B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
12 GB
12 GB tier →fits · 0.2 GB free · up to 16K ctx
10 GPUs
RTX 3080 Ti~67Simulate output on RTX 3080 TiRTX 5070~50Simulate output on RTX 5070RTX 4070~37Simulate output on RTX 407020 GB
fits · 8.2 GB free · up to 32K ctx
1 GPU
RX 7900 XT~54Simulate output on RX 7900 XT32–40 GB
fits · 20.2 GB free · up to 64K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~132Simulate output on RTX 5090M5 Max36 GB~37Simulate output on Mac Studio M5 Max (36 GB)M532 GB~12Simulate output on MacBook Air 13" M5 (32 GB)Orin~15Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~10Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Nemotron Labs Diffusion 14B
Nemotron family · 42 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| Qwen1.5 14BAlibaba | — | 22.2 GB | ~63 | same size |
| Baichuan2 13B Basebaichuan-inc | — | 11.5 GB | ~76 | same size |
| Llama 2 13B Chat HFMeta | — | 11.5 GB | ~76 | same size |
| Vicuna 13B V1.3LMSYS | — | 8.6 GB | ~76 | same size |
| Vllm Translategemma 12B ITchbae624 | — | 9.8 GB | ~73 | same size |
Specifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 13.5B
- Architecture
- NemotronLabsDiffusionModel
- Context Length
- 262,144 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-04-22
- License
- Other
Frequently Asked Questions
How much VRAM does Nemotron Labs Diffusion 14B need?
Nemotron Labs Diffusion 14B requires 8.8 GB of VRAM at Q4_K_M, or 27.7 GB at BF16. Full 262K context adds up to 53.3 GB (62.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 13.5B × 4.8 bits ÷ 8 = 8.1 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 54 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Nemotron Labs Diffusion 14B?
Yes, at Q8_0 (14.2 GB) or lower. Higher quantizations like BF16 (27.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Nemotron Labs Diffusion 14B?
For Nemotron Labs Diffusion 14B, Q4_K_M (8.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (10.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Nemotron Labs Diffusion 14B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Nemotron Labs Diffusion 14B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 8.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Nemotron Labs Diffusion 14B locally?
Yes — Nemotron Labs Diffusion 14B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Nemotron Labs Diffusion 14B?
At Q4_K_M, Nemotron Labs Diffusion 14B can reach ~544 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~74 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 ÷ 8.82 × 0.65 = ~590 tok/s
Estimated speed at Q4_K_M (8.8 GB)
Real-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 Q4_K_M, the download is about 8.10 GB. The full-precision BF16 version is 27.01 GB. The smallest option (Q2_K) is 5.74 GB.
Which GPUs can run Nemotron Labs Diffusion 14B?
40 consumer GPUs can run Nemotron Labs Diffusion 14B at Q4_K_M. Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
Which devices can run Nemotron Labs Diffusion 14B?
150 devices with unified memory can run Nemotron Labs Diffusion 14B at Q4_K_M (8.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.