How much VRAM does SmolLM3 3B need?

SmolLM3 3B 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 SmolLM3 3B on a consumer GPU?

Yes, SmolLM3 3B 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 SmolLM3 3B's context window?

SmolLM3 3B supports a maximum context window of 8,192 tokens. The default context is 2,048 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 SmolLM3 3B?

For SmolLM3 3B, 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 SmolLM3 3B compare to similar models?

SmolLM3 3B is a 3.0B Dense model from the SmolLM family. Browse other SmolLM 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.

SmolLMDenseApache 2.0

SmolLM3 3B

SmolLM3 3B is a dense transformer language model from the SmolLM family, containing 3B parameters across 32 layers. It supports up to 8K tokens of context with a hidden dimension of 2560 and 8 KV heads for efficient grouped-query attention …

3.0B

Parameters

Max Context

Dense

Architecture

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Released

Text

Modality

About SmolLM3 3B

On-Device

Technical Specifications

Total Parameters3.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 2,560

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	8K ctx
Q4_K_M0.50 B/W ~97% of FP16	1.68Consumer GPU	2.55Consumer GPU
Q8_01.00 B/W ~100% of FP16	3.23Consumer GPU	4.10Consumer GPU
F162.00 B/W Reference	6.33Consumer GPU	7.20Consumer GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Find the right GPU for SmolLM3 3B

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