How much VRAM does Qwen 3.5 0.8B need?

Qwen 3.5 0.8B 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 3.5 0.8B on a consumer GPU?

Yes, Qwen 3.5 0.8B 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 12 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Qwen 3.5 0.8B's context window?

Qwen 3.5 0.8B 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 0.8B?

For Qwen 3.5 0.8B, 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 3.5 0.8B compare to similar models?

Qwen 3.5 0.8B is a 0.8B 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 0.8B

Qwen 3.5 0.8B is a dense transformer language model from the Qwen family, containing 0.8B parameters across 6 layers. It supports up to 262K tokens of context with a hidden dimension of 1024 and 2 KV heads for efficient grouped-query attent…

800M

Parameters

256K

Max Context

Dense

Architecture

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Released

Text

Modality

About Qwen 3.5 0.8B

On-DeviceBasic Chat

Technical Specifications

Total Parameters800M

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 1,024

Transformer Layers6

Attention Heads8

KV Headsn_kv = 2

Head Dimensiond_head = 256

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

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	0.43Consumer GPU	2.70Consumer GPU	3.41Consumer GPU
Q8_01.00 B/W ~100% of FP16	0.84Consumer GPU	3.12Consumer GPU	3.83Consumer GPU
F162.00 B/W Reference	1.67Consumer GPU	3.94Consumer GPU	4.65Consumer 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