How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B 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 3.5 9B on a consumer GPU?

Yes, Qwen 3.5 9B 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 3.5 9B's context window?

Qwen 3.5 9B supports a maximum context window of 262,144 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 3.5 9B?

For Qwen 3.5 9B, 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 3.5 9B compare to similar models?

Qwen 3.5 9B is a 9.0B 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 3.5 9B

9.0B

Parameters

256K

Max Context

Dense

Architecture

Feb 18, 2026

Released

Text

Modality

About Qwen 3.5 9B

Qwen 3.5 9B introduces a hybrid architecture combining Gated DeltaNet linear attention layers with standard full attention — only 25% of layers use KV-cached attention, dramatically reducing memory overhead for long contexts. At 9B parameters supporting 262K context (extensible to 1M with YaRN), it handles extremely long documents and multi-turn conversations at a fraction of the VRAM cost of traditional architectures. Apache 2.0 licensed. An excellent choice for long-context RAG, document analysis, and extended agent sessions.

Long ContextRAGDocument AnalysisAgenticCommercial

Technical Specifications

Total Parameters9.0B

ArchitectureDense

Attention TypeHybrid Gated DeltaNet + Full Attention (25% layers)

Hidden Dimensiond = 4,096

Transformer Layers8

Attention Heads16

KV Headsn_kv = 4

Head Dimensiond_head = 256

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingDual RoPE (local + global)

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	4.68Consumer GPU	10.76Consumer GPU	12.65Consumer GPU
Q8_01.00 B/W ~100% of FP16	9.34Consumer GPU	15.41Consumer GPU	17.30Consumer GPU
F162.00 B/W Reference	18.64Consumer GPU	24.71Datacenter GPU	26.61Datacenter 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