How much VRAM does Qwen 3 8B need?

Qwen 3 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 Qwen 3 8B on a consumer GPU?

Yes, Qwen 3 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 16 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Qwen 3 8B's context window?

Qwen 3 8B supports a maximum context window of 131,072 tokens. The default context is 32,768 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 Qwen 3 8B?

For Qwen 3 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 Qwen 3 8B compare to similar models?

Qwen 3 8B is a 8.2B Dense model from the Qwen family. Browse other Qwen 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.

QwenDenseApache 2.0

Qwen 3 8B

8.2B

Parameters

128K

Max Context

Dense

Architecture

Apr 29, 2025

Released

Text

Modality

About Qwen 3 8B

Qwen 3 8B is a major leap over Qwen 2.5 7B with hybrid reasoning capabilities — it can toggle between quick-thinking mode and extended chain-of-thought reasoning. At 8.19B parameters with 128K context and Apache 2.0 license, it is arguably the best all-round 8B-class local model as of mid-2025. The thinking mode significantly improves math and logic performance at the cost of higher token usage. Excellent for both interactive chat and structured problem-solving.

General PurposeReasoningCodeMultilingualCommercial

Technical Specifications

Total Parameters8.2B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 4,096

Transformer Layers36

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	4.37Consumer GPU	22.23Consumer GPU
Q8_01.00 B/W ~100% of FP16	8.61Consumer GPU	26.47Datacenter GPU
F162.00 B/W Reference	17.07Consumer GPU	34.93Datacenter GPU

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