# Qwen3.5-9B: Self-Hosting Hardware, Scenarios and Commercial License

> The local and edge-friendly baseline for private multimodal assistants, extraction and moderate-volume APIs.

- Verified: 2026-09-12
- Released: 2026-02-16
- Canonical: https://chinaapi.ai/open-model-deployment/qwen3.5-9b/
- Back to directory: https://chinaapi.ai/open-model-deployment/
- Machine-readable dataset: https://chinaapi.ai/data/open-model-deployment.json

## Model facts

- Vendor: Alibaba Qwen
- Parameters: 9B total; 9B active
- Native precision: BF16; multiple quantized formats available
- Context: 256K tokens
- Category: General and agentic foundation models
- Modalities: text, image, video → text
- Serving paths: Transformers, vLLM, SGLang, KTransformers, llama.cpp
- Official weights: https://huggingface.co/Qwen/Qwen3.5-9B?utm_source=chinaapi&utm_medium=research&utm_campaign=open-model-deployment
- Model documentation: https://huggingface.co/Qwen/Qwen3.5-9B?utm_source=chinaapi&utm_medium=research&utm_campaign=open-model-deployment

## Deployment evidence

- **Official launch / minimum — evidence E:** A 24GB-class GPU is a reasonable BF16 short-context planning target, but the vendor does not label this an official minimum.
- **200-person team — evidence E:** Benchmark one or two 24–48GB workers before considering larger models; exact replicas depend on output length.
- **Commercial API — evidence C:** Official Transformers, vLLM and SGLang examples are published; add redundant replicas for availability. Source: https://huggingface.co/Qwen/Qwen3.5-9B?utm_source=chinaapi&utm_medium=research&utm_campaign=open-model-deployment

## Scenario evidence

- **Local multimodal assistant — ChinaAPI inference:** The 9B dense shape is the most accessible formal model in this ledger.
- **Visual extraction and classification — Vendor-stated:** The official model is a unified vision-language foundation model. Source: https://huggingface.co/Qwen/Qwen3.5-9B?utm_source=chinaapi&utm_medium=research&utm_campaign=open-model-deployment
- **Cost-sensitive private API — ChinaAPI inference:** A better first benchmark target than frontier-scale MoE models when concurrency and budget dominate.

## Commercial-use check

- License: Apache-2.0 — https://huggingface.co/Qwen/Qwen3.5-9B/blob/main/LICENSE?utm_source=chinaapi&utm_medium=research&utm_campaign=open-model-deployment
- MaaS / hosted service: No model-specific MaaS restriction identified in Apache-2.0.
- Attribution: Provide the license and notices, preserve attribution notices, and mark modified files.

## Avoid or validate first

- Assuming 1M extended context fits a 24GB card
- Highest-complexity long-horizon agents without task-specific evaluation

## Known limitations and open questions

- 24GB guidance is an estimate, not ChinaAPI reproduction
- Native 262K and extended 1M contexts materially increase KV-cache demand

## Evidence boundary

Loading weights, completing a first forward pass and meeting a production latency/SLA are separate thresholds. The 200-person and commercial tiers require workload-specific measurement. License summaries are product research, not legal advice.

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