Embedding
How to deploy Qwen3-Embedding-0.6B on a GPU cloud
A 596M-parameter text embedding model. Full specs, license and use cases.
Qwen3-Embedding-0.6B size and hardware requirements
596M
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
1.3 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 1.1 GB | 1.3 GB | A16 | 1 | $0.059/hr |
| FP8 (quantized) | 0.6 GB | 0.7 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 0.3 GB | 0.3 GB | A16 | 1 | $0.059/hr |
How to run Qwen3-Embedding-0.6B
Run Qwen3-Embedding-0.6B with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve Qwen/Qwen3-Embedding-0.6B --task embed --tensor-parallel-size 1Deploy Qwen3-Embedding-0.6B on Aquanode
Aquanode has no one-click deploy template for Qwen3-Embedding-0.6B; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× A16 or larger).
- 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.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod