Qwen3.5 35B-A3B: local hardware requirements and GPU compatibility
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Local AI model

Qwen3.5 35B-A3B

by Alibaba Qwen

The weights fit entirely in accelerator memory on 4 of 22 reference machines. The KV cache or runtime state is not fully modelled, so this is a memory floor, not a "runs" verdict.

36B Parameters 256k Maximum context 1 Quantization Partial Compatibility coverage
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Overview

Publisher
Alibaba Qwen
Family
Qwen
Series
Qwen3.5
Task
Multimodal
Architecture
mixture of experts · qwen3_5_moe_text
Parameters
36B
Maximum context
262,144 tokens
Licence
apache-2.0
Published
2026-02-24
Formats
gguf, safetensors
KV cache per token (f16)
20,480 B · attention layers only: recurrent state not included
Compatibility coverage
Partial: memory floor only
Upstream repository
Qwen/Qwen3.5-35B-A3B
Official page
https://qwen.ai/

Quantizations and file sizes

Quantization Runtime Weights Memory floor at 4k Identity
Q4_K_Mgguf ollamaollama run qwen3.5:35b-a3b 22.2 GB 22.3 GB digest verified

Sizes come from the runtime registry or repository listing. The memory floor adds the KV cache for 4,096 tokens when that figure cannot over-count; it is an estimate, not a requirement.

Runtimes

Compatibility on reference machines

8k context, 32 GB system RAM, best catalogued quantization per machine.

Machine Memory Answer Known facts
Radeon RX 7900 XTX 24GB 24.0 GB VRAM Potential: weights fit not verified · Q4_K_M Weights 22.2 GB vs 24.0 GB VRAM: fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 3090 24GB 24.0 GB VRAM Potential: weights fit not verified · Q4_K_M Weights 22.2 GB vs 24.0 GB VRAM: fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 4090 24GB 24.0 GB VRAM Potential: weights fit not verified · Q4_K_M Weights 22.2 GB vs 24.0 GB VRAM: fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 5090 32GB 32.0 GB VRAM Potential: weights fit not verified · Q4_K_M Weights 22.2 GB vs 32.0 GB VRAM: fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 3060 Laptop GPU 6GB 6.00 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 6.00 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
Radeon RX 7600 8GB 8.00 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 8.00 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 4060 8GB 8.00 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 8.00 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 3080 10GB 10.0 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 10.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 3060 12GB 12.0 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 12.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 4070 12GB 12.0 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 12.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 5070 12GB 12.0 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 12.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
Radeon RX 7800 XT 16GB 16.0 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 16.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
Radeon RX 9070 XT 16GB 16.0 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 16.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 4060 Ti 16GB 16.0 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 16.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 4080 16GB 16.0 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 16.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 5060 Ti 16GB 16.0 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 16.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
GeForce RTX 5080 16GB 16.0 GB VRAM Potential: weights need offload not verified · Q4_K_M Weights 22.2 GB vs 16.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not included
Intel Arc B580 12GB 12.0 GB VRAM Not enough data not verified · Q4_K_M Weights 22.2 GB vs 12.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not includedruntime support on this machine: unknown
Intel Arc A770 16GB 16.0 GB VRAM Not enough data not verified · Q4_K_M Weights 22.2 GB vs 16.0 GB VRAM: do not fitKV cache at 8k: attention layers only: recurrent state not includedruntime support on this machine: unknown
Mac mini M4 24GB 24.0 GB unified Not enough data not verified · Q4_K_M Weights 22.2 GB vs 24.0 GB unified: fitKV cache at 8k: attention layers only: recurrent state not included
Mac mini M4 Pro 24GB 24.0 GB unified Not enough data not verified · Q4_K_M Weights 22.2 GB vs 24.0 GB unified: fitKV cache at 8k: attention layers only: recurrent state not included
MacBook Pro 14-inch M4 Max 36GB 36.0 GB unified Not enough data not verified · Q4_K_M Weights 22.2 GB vs 36.0 GB unified: fitKV cache at 8k: attention layers only: recurrent state not included

Definitive answers ("runs", "does not fit") come from the calibrated engine or a physical run. "Potential" rows only compare the weights with memory: the rest of the requirement is not modelled, so they are not verdicts. Unknown is never a failure.

Hardware for this model

At least 24 GB of VRAM is needed just to hold the smallest weights (22.2 GB). The complete requirement is higher and not modelled yet.

  • Smallest catalogued artifact: 22.2 GB of weights (Q4_K_M).
  • A GPU with at least 24 GB of VRAM holds those weights.
  • Disk space: 500 GB or more to keep this model and its variants.
  • Not modelled yet: attention layers only: recurrent state not included.
Explore 24 GB VRAM GPUsExplore 500 GB NVMe SSDsExplore desktops with a 24 GB GPU
These links open a hardware category search, not a specific product recommendation. Affiliate link — I may earn a commission from qualifying purchases. These links never change a compatibility answer. They follow the memory the data defends, not commission.

Sources and evidence

0 measured, 25 sourced, 3 estimated and 3 unknown fields.

Catalogue record verified 2026-09-16.

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