How much VRAM does Qwen 3.5 27B need?

Qwen 3.5 27B 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 27B on a consumer GPU?

Yes, Qwen 3.5 27B can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 24 GB GPU. For best performance, use a 24 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Qwen 3.5 27B's context window?

Qwen 3.5 27B 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 27B?

For Qwen 3.5 27B, start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does Qwen 3.5 27B compare to similar models?

Qwen 3.5 27B is a 27.0B 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 27B

27.0B

Parameters

256K

Max Context

Dense

Architecture

Feb 18, 2026

Released

Text

Modality

About Qwen 3.5 27B

Qwen 3.5 27B is the dense flagship of the Qwen 3.5 hybrid architecture family. At 27B parameters with only 25% of layers using full KV-cache attention, it handles 262K contexts with dramatically less memory than traditional architectures. Delivers quality approaching dense 70B models while fitting comfortably on 24 GB GPUs at Q4_K_M (~15 GB for weights + minimal KV cache). The Gated DeltaNet hybrid approach represents the future direction of efficient long-context LLMs. Apache 2.0.

Long ContextCodeReasoningAgenticCommercial

Technical Specifications

Total Parameters27.0B

ArchitectureDense

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

Hidden Dimensiond = 5,120

Transformer Layers16

Attention Heads24

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	14.02Consumer GPU	26.16Datacenter GPU	29.96Datacenter GPU
Q8_01.00 B/W ~100% of FP16	27.97Datacenter GPU	40.12Datacenter GPU	43.91Datacenter GPU
F162.00 B/W Reference	55.89Datacenter GPU	68.03Datacenter GPU	71.82Datacenter 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