How much VRAM does Qwen 3.6 35B-A3B (MoE) need?

Qwen 3.6 35B-A3B (MoE) 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 3.6 35B-A3B (MoE) on a consumer GPU?

Yes, Qwen 3.6 35B-A3B (MoE) 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 3.6 35B-A3B (MoE)'s context window?

Qwen 3.6 35B-A3B (MoE) supports a maximum context window of 262,144 tokens. The default context is 131,072 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 3.6 35B-A3B (MoE)?

For Qwen 3.6 35B-A3B (MoE), 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 3.6 35B-A3B (MoE) compare to similar models?

Qwen 3.6 35B-A3B (MoE) is a 35.0B MoE 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.

QwenMoEApache 2.0

Qwen 3.6 35B-A3B (MoE)

35.0B

Parameters

3.0B

Active

256K

Max Context

MoE

Architecture

—

Released

Text

Modality

About Qwen 3.6 35B-A3B (MoE)

Qwen 3.6 35B-A3B (MoE) is a mixture-of-experts (MoE) transformer language model from the Qwen family, containing 35B parameters across 10 layers. It has 35B total parameters loaded into VRAM with 3B active per token. It supports up to 262K tokens of context with a hidden dimension of 2048 and 2 KV heads for efficient grouped-query attention (GQA). Apache 2.0. MoE: 256 experts, 8+1 active. DeltaNet+GA hybrid. 262K ctx, ext to ~1M with YaRN. SWE-bench 73.4.

ResearchEnterprise

Technical Specifications

Total Parameters35.0B

Active Parameters3.0B per token

ArchitectureMixture of Experts

Total Experts3

Attention TypeGQA (MoE)

Hidden Dimensiond = 2,048

Transformer Layers10

Attention Heads16

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	18.11Consumer GPU	21.91Consumer GPU	23.09Consumer GPU
Q8_01.00 B/W ~100% of FP16	36.20Datacenter GPU	40.00Datacenter GPU	41.18Datacenter GPU
F162.00 B/W Reference	72.38Datacenter GPU	76.18Datacenter GPU	77.36Datacenter 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