How much VRAM does Qwen 3.5 397B-A17B (MoE) need?

Qwen 3.5 397B-A17B (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 3.5 397B-A17B (MoE) on a consumer GPU?

Qwen 3.5 397B-A17B (MoE) is a 397B parameter model and requires datacenter or multi-GPU setups. At Q4_K_M, it needs approximately 0.0 GB VRAM, which exceeds consumer GPU limits. It can run on H100 (80 GB) or A100 (80 GB) datacenter GPUs, or across multiple consumer GPUs with tensor parallelism.

What is Qwen 3.5 397B-A17B (MoE)'s context window?

Qwen 3.5 397B-A17B (MoE) supports a maximum context window of 262,144 tokens. The default context is 16,384 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 397B-A17B (MoE)?

For Qwen 3.5 397B-A17B (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 3.5 397B-A17B (MoE) compare to similar models?

Qwen 3.5 397B-A17B (MoE) is a 397.0B 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 3.5 397B-A17B (MoE)

397.0B

Parameters

17.0B

Active

256K

Max Context

MoE

Architecture

—

Released

Text

Modality

About Qwen 3.5 397B-A17B (MoE)

Qwen 3.5 397B-A17B (MoE) is a mixture-of-experts (MoE) transformer language model from the Qwen family, containing 397B parameters across 15 layers. It has 397B total parameters loaded into VRAM with 17B active per token. It supports up to 262K tokens of context with a hidden dimension of 4096 and 2 KV heads for efficient grouped-query attention (GQA). Apache 2.0. MoE flagship: 512 experts. DeltaNet+MoE hybrid. Server class.

ResearchEnterprise

Technical Specifications

Total Parameters397.0B

Active Parameters17.0B per token

ArchitectureMixture of Experts

Total Experts17

Attention TypeGQA (MoE)

Hidden Dimensiond = 4,096

Transformer Layers15

Attention Heads32

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	205.2Cluster / Multi-GPU	210.9Cluster / Multi-GPU	212.7Cluster / Multi-GPU
Q8_01.00 B/W ~100% of FP16	410.4Cluster / Multi-GPU	416.1Cluster / Multi-GPU	417.9Cluster / Multi-GPU
F162.00 B/W Reference	820.8Cluster / Multi-GPU	826.5Cluster / Multi-GPU	828.3Cluster / Multi-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