How much VRAM does Nemotron 3 Nano 30B-A3B (MoE) need?

Nemotron 3 Nano 30B-A3B (MoE) requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run Nemotron 3 Nano 30B-A3B (MoE) on a consumer GPU?

Yes, Nemotron 3 Nano 30B-A3B (MoE) can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 24 GB GPU. For best performance, use a 24 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Nemotron 3 Nano 30B-A3B (MoE)'s context window?

Nemotron 3 Nano 30B-A3B (MoE) supports a maximum context window of 262,144 tokens. The default context is 16,384 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for Nemotron 3 Nano 30B-A3B (MoE)?

For Nemotron 3 Nano 30B-A3B (MoE), start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does Nemotron 3 Nano 30B-A3B (MoE) compare to similar models?

Nemotron 3 Nano 30B-A3B (MoE) is a 30.0B MoE model from the Nvidia family. Browse other Nvidia models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

NvidiaMoENVIDIA Open Model License

Nemotron 3 Nano 30B-A3B (MoE)

30.0B

Parameters

3.0B

Active

256K

Max Context

MoE

Architecture

—

Released

Text

Modality

About Nemotron 3 Nano 30B-A3B (MoE)

Nemotron 3 Nano 30B-A3B (MoE) is a mixture-of-experts (MoE) transformer language model from the Nvidia family, containing 30B parameters across 40 layers. It has 30B total parameters loaded into VRAM with 3B active per token. It supports up to 262K tokens of context with a hidden dimension of 2560 and 8 KV heads for efficient grouped-query attention (GQA). Nemotron Open Model License. MoE. Up to 1M context. Efficient local reasoning/agents.

ReasoningAgentic

Technical Specifications

Total Parameters30.0B

Active Parameters3.0B per token

ArchitectureMixture of Experts

Total Experts3

Attention TypeGQA (MoE)

Hidden Dimensiond = 2,560

Transformer Layers40

Attention Heads32

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	195K ctx	256K ctx
Q4_K_M0.50 B/W ~97% of FP16	15.66Consumer GPU	46.02Datacenter GPU	55.51Datacenter GPU
Q8_01.00 B/W ~100% of FP16	31.17Datacenter GPU	61.53Datacenter GPU	71.01Datacenter GPU
F162.00 B/W Reference	62.18Datacenter GPU	92.54Cluster / Multi-GPU	102.0Cluster / Multi-GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Other Nvidia Models

View All

Nemotron-Nano 9B v2

Params

9.0B

Layers

Context

128K

Nemotron 3 Nano 4B

Params

4.0B

Layers

Context

256K

Llama-Nemotron 8B

Params

8.0B

Layers

Context

128K

Find the right GPU for Nemotron 3 Nano 30B-A3B (MoE)

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

Open VRAM Calculator Best GPU Guide VRAM Guide