How much VRAM does Llama 3.2 1B need?

Llama 3.2 1B 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 3.2 1B on a consumer GPU?

Yes, Llama 3.2 1B 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 3.2 1B's context window?

Llama 3.2 1B 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 3.2 1B?

For Llama 3.2 1B, 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 3.2 1B compare to similar models?

Llama 3.2 1B is a 1.2B Dense model from the Llama family. Browse other Llama 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.

LlamaDenseLlama 3.2 Community License

Llama 3.2 1B

1.2B

Parameters

128K

Max Context

Dense

Architecture

Sep 25, 2024

Released

Text

Modality

About Llama 3.2 1B

Llama 3.2 1B is Meta's ultra-compact model designed for on-device and edge deployment. At just 1.24B parameters across 16 layers, it can run on CPU, mobile phones, and Raspberry Pi-class hardware. While limited in reasoning depth, it handles basic chat, summarization, and classification tasks competently. The Q4_K_M quantized version uses only ~700 MB of VRAM.

On-DeviceEdgeClassificationBasic Chat

Technical Specifications

Total Parameters1.2B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 2,048

Transformer Layers16

Attention Heads32

KV Headsn_kv = 8

Head Dimensiond_head = 64

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	0.67Consumer GPU	4.64Consumer GPU
Q8_01.00 B/W ~100% of FP16	1.31Consumer GPU	5.28Consumer GPU
F162.00 B/W Reference	2.59Consumer GPU	6.56Consumer GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Other Llama Models

View All

Llama 3.2 3B

Params

3.2B

Layers

Context

128K

Llama 3.1 8B

Params

8.0B

Layers

Context

128K

Llama 3.1 70B

Params

70.6B

Layers

Context

128K

Llama 3.1 405B

Params

405.0B

Layers

126

Context

128K

Llama 3.3 70B

Params

70.6B

Layers

Context

128K

Llama 4 Scout (MoE)

Params

109.0B

Layers

Context

256K

17.0B active

Find the right GPU for Llama 3.2 1B

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