LLM
How to deploy SmolLM2-360M-Instruct on a GPU cloud
A 362M language model for chat and instruction-following. Full specs, license and use cases.
SmolLM2-360M-Instruct size and hardware requirements
362M
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
0.8 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 0.7 GB | 0.8 GB | A16 | 1 | $0.059/hr |
| FP8 (quantized) | 0.3 GB | 0.4 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 0.2 GB | 0.2 GB | A16 | 1 | $0.059/hr |
How to run SmolLM2-360M-Instruct
Run SmolLM2-360M-Instruct with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve HuggingFaceTB/SmolLM2-360M-Instruct --tensor-parallel-size 1Run SmolLM2-360M-Instruct with GGUF quantizations
Prebuilt GGUF weights published at unsloth/SmolLM2-360M-Instruct-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/SmolLM2-360M-Instruct-GGUFSource: https://huggingface.co/unsloth/SmolLM2-360M-Instruct-GGUF
Deploy SmolLM2-360M-Instruct on Aquanode
Aquanode has no one-click deploy template for SmolLM2-360M-Instruct; 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