How much VRAM does Llama-Nemotron 8B need?

Llama-Nemotron 8B 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 Llama-Nemotron 8B on a consumer GPU?

Yes, Llama-Nemotron 8B can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 12 GB GPU. For best performance, use a 12 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Llama-Nemotron 8B's context window?

Llama-Nemotron 8B supports a maximum context window of 131,072 tokens. The default context is 8,192 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 Llama-Nemotron 8B?

For Llama-Nemotron 8B, Q4_K_M is the recommended quantization — it provides an excellent quality-to-VRAM ratio at ~0.0 GB. Q8_0 offers slightly better quality at ~2× the VRAM. Q4_K_M is the sweet spot for most users.

How does Llama-Nemotron 8B compare to similar models?

Llama-Nemotron 8B is a 8.0B Dense 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.

NvidiaDenseOpen Weights

Llama-Nemotron 8B

Llama-Nemotron 8B is a dense transformer language model from the Nvidia family, containing 8B parameters across 32 layers. It supports up to 131K tokens of context with a hidden dimension of 4096 and 8 KV heads for efficient grouped-query a…

8.0B

Parameters

128K

Max Context

Dense

Architecture

—

Released

Text

Modality

About Llama-Nemotron 8B

Reasoning

Technical Specifications

Total Parameters8.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 4,096

Transformer Layers32

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	128K ctx
Q4_K_M0.50 B/W ~97% of FP16	4.26Consumer GPU	20.14Consumer GPU
Q8_01.00 B/W ~100% of FP16	8.40Consumer GPU	24.27Datacenter GPU
F162.00 B/W Reference	16.67Consumer GPU	32.54Datacenter GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

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Find the right GPU for Llama-Nemotron 8B

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