What GPU do I need to run jinaai/ReaderLM-v2?
A 1.5B language model for chat and instruction-following. 1.5B parameters, published in BF16. View on Hugging Face
ReaderLM-v2 is published by jinaai on Hugging Face, with 1,454 downloads and 818 likes to date. It's a Qwen2ForCausalLM model built for text-generation, published natively in BF16.
What ReaderLM-v2 is
ReaderLM-v2 is a 1.5B-parameter language model published by Jina AI on Hugging Face. It reads images alongside text. It is released under CC BY-NC 4.0.
License note: non-commercial use only under Creative Commons terms. Facts in this section are sourced from ReaderLM-v2'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, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 2.9 GB | 3.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 1.4 GB | 1.7 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 0.7 GB | 0.9 GB | RTX 5060 Ti | 1 | $0.110/hr |
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.
Cheapest way to run ReaderLM-v2 at its published (BF16) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
ReaderLM-v2: common questions
How much VRAM does ReaderLM-v2 need?
3.5 GB at BF16, 1.7 GB at FP8 (quantized), 0.9 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 2.9 GB of weights plus inference overhead is the whole requirement.
How many copies of ReaderLM-v2 fit on one RTX 5060 Ti?
4, by VRAM alone. That card carries 16.0 GB and one copy needs 3.5 GB at BF16, on a live rate of $0.110/hr for the whole card. Throughput is not modelled here, so 4 copies is not 4 times the requests served.
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 5060 Ti 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 podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
