What GPU do I need to run Qwen/Qwen3.5-9B?

What Qwen3.5-9B is

Qwen3.5-9B is a 9.7B-parameter language model published by Alibaba (Qwen) on Hugging Face. It is released under Apache 2.0.

License note: fully permissive, no gating and no usage restrictions. Facts in this section are sourced from Qwen3.5-9B's Hugging Face model card, not benchmarked by Aquanode.

What it's used for

• Chat assistants
• Instruction following
• Synthetic data generation

VRAM required & cheapest live GPU fit

Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.

A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.

INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.

Qwen3.5-9B: KV cache by context length

The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.

Hybrid: only the full-attention layers cache per token; the other layers keep a fixed-size state. Cached per token: 32,768 bytes at 16-bit.

Computed from the layer, head and window counts in the model's own configuration (Qwen/Qwen3.5-9B), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.

Qwen3.5-9B: common questions

How to run Qwen3.5-9B

vllm serve Qwen/Qwen3.5-9B --tensor-parallel-size 1

ollama run qwen3.5:9b

llama-server -hf unsloth/Qwen3.5-9B-GGUF

Deploy Qwen3.5-9B on Aquanode

Aquanode has no one-click deploy template for Qwen3.5-9B; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.

1. Launch a bare GPU pod sized to the requirement above (1× RTX A5000 or larger).
2. Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
3. Run the command and connect to the resulting endpoint.

Launch a GPU pod

More Qwen3.5 models

• Qwen3.5-9B-AWQ (9.7B, BF16)
• Qwen3.5-9B-Base (9.7B, BF16)
• Qwen3.8-9B-Distill (9.7B, BF16)
• Qwen3.5-9B-FP8-dynamic (9.4B, F8_E4M3)
• Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSORED (9.4B, BF16)

All 43 Qwen3.5 models: VRAM and GPU requirements

Lineage

Based on Qwen3.5-9B-Base.

Quantized and fine-tuned versions

• Qwen3.5-9B-AWQ (AWQ)
• Qwen3.5-9B-FP8-dynamic
• JEV-9B
• Qwen3.5-9B-NVFP4
• Qwen3.5-9B-DFlash
• Qwythos-9B-Claude-Mythos-5-1M
• Qwen3.8-9B-Distill (distilled)
• MiMo-V2.6-Distill-Qwen-9B (distilled)
• clef-flash
• WeMM-Embedding-9B
• LensVLM-9B
• Gmcoder

Also published as: unsloth/Qwen3.5-9B. Identical copies of this model under another account.

Alternatives at this size

Other models for image-text-to-text within about a third of Qwen3.5-9B's 9.7B parameters, from other model lines.

• llava-1.5-7b-hf (7.1B, F16)
• gemma-3-12b-it (12.2B, BF16)
• UI-TARS-1.5-7B (8.3B, F32)
• Cosmos-Reason2-8B (8.8B, BF16)
• Ovis2.5-9B (9.2B, BF16)

More on Qwen3.5-9B

• Fits on a 24 GB GPU at BF16: every model that fits in 24 GB.

• Fits on a 12 GB GPU at FP8: every model that fits in 12 GB.

• Fits on an 8 GB GPU at INT4: every model that fits in 8 GB.

• Best vision-language models: how Qwen3.5-9B ranks against the rest.

• How to run Qwen3.5 and how to fine-tune Qwen3.5.