LLM
How to deploy ReaderLM-v2 on a GPU cloud
A 1.5B language model for chat and instruction-following. Full specs, license and use cases.
ReaderLM-v2 size and hardware requirements
1.5B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
3.5 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 2.9 GB | 3.5 GB | RTX 4070 Super | 1 | $0.110/hr |
| FP8 (quantized) | 1.4 GB | 1.7 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 0.7 GB | 0.9 GB | A16 | 1 | $0.059/hr |
How to run ReaderLM-v2
Run ReaderLM-v2 with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve jinaai/ReaderLM-v2 --tensor-parallel-size 1Run ReaderLM-v2 with GGUF quantizations
Prebuilt GGUF weights published at mradermacher/ReaderLM-v2-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf mradermacher/ReaderLM-v2-GGUFSource: https://huggingface.co/mradermacher/ReaderLM-v2-GGUF
Deploy ReaderLM-v2 on Aquanode
Aquanode has no one-click deploy template for ReaderLM-v2; 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× RTX 4070 Super 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