What GPU do I need to run jinaai/jina-code-embeddings-1.5b?
1.5B parameters, published in BF16. View on Hugging Face
Set up jina-code-embeddings-1.5bjina-code-embeddings-1.5b is published by jinaai on Hugging Face, with 23,466 downloads and 53 likes to date. It's a Qwen2ForCausalLM model built for feature-extraction, published natively in BF16.
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 jina-code-embeddings-1.5b 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.
jina-code-embeddings-1.5b: common questions
How much VRAM does jina-code-embeddings-1.5b 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 jina-code-embeddings-1.5b 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.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen2.5 models
- Qwen2.5-1.5B-Instruct (1.5B, BF16)
- Qwen2.5-1.5B (1.5B, BF16)
- Qwen2.5-Math-1.5B (1.5B, BF16)
- Qwen2.5-Coder-1.5B-Instruct (1.5B, BF16)
- Qwen2.5-Math-1.5B-Instruct (1.5B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
