voyage-4-nano
voyage-4-nano is a lightweight text embedding model (346M parameters) from Voyage AI designed for semantic search and retrieval. It supports multilingual content, offers flexible embedding dimensions (256–2048D), quantization-aware outputs (float32 to binary), and maintains a shared embedding space with larger Voyage 4 variants, enabling model switching without re-indexing. Licensed under Apache 2.0 and ungated for immediate use.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | voyageai |
| Parameters | 346M |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | feature-extraction |
| Gated on HuggingFace | No |
| Downloads | 173.5k |
| Likes | 125 |
| Last updated | 2026-03-02 |
| Source | voyageai/voyage-4-nano |
What voyage-4-nano is
A bidirectional transformer-based embedding model featuring Matryoshka Representation Learning (MRL) for dimension flexibility and quantization-aware training for output precision control. Context length is 32,000 tokens. Supports inference via Hugging Face Transformers, Sentence Transformers, and vLLM. Compatible with flash_attention_2 and bfloat16 for GPU acceleration on modern hardware (A100, H100, RTX 30/40 series). Custom code required; distributed as safetensors.
Run voyage-4-nano locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="voyageai/voyage-4-nano")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: 2–4 GB VRAM (bfloat16, single inference). For batch processing: 8–16 GB VRAM recommended. CPU-only inference feasible but slow; GPU strongly advised. Flash Attention 2 requires A100, H100, RTX 30/40 series or newer. Context length up to 32K requires proportional memory scaling.
Card does not explicitly document LoRA, QLoRA, or instruction-tuning support. Nano model (180M non-embedding + 160M embedding parameters) is lightweight enough for standard fine-tuning on moderate GPUs (24 GB+), but no built-in framework mentioned. Requires custom implementation or community tooling.
When to avoid it — and what to weigh
- Real-time Generative Tasks — This is a feature-extraction (embedding) model, not a generative LLM. Do not use for text generation, summarization, or conversational AI.
- Ultra-Low Latency Edge Deployment (Sub-50ms) — Even nano variant requires significant compute. Binary quantization reduces footprint but may not meet ultra-strict latency SLAs without specialized optimization.
- Domain-Specific Embeddings Without Fine-tuning — Card mentions no domain-specific variants. If your use case requires highly specialized embeddings (legal, medical, scientific), evaluate fine-tuning feasibility or specialized models first.
- Proprietary / Closed-Source Deployment Restrictions — Apache 2.0 is permissive but requires attribution and disclosure of modifications. Verify internal IP policies permit this before adopting.
License & commercial use
Apache License 2.0 (OSI-approved, permissive). Allows commercial use, modification, distribution, and private use with conditions: retain attribution, include license copy, and disclose significant modifications. No patent indemnity.
Apache 2.0 is an OSI-approved permissive license that explicitly permits commercial use. No restrictions on proprietary applications, closed-source deployment, or service offerings. Standard obligations apply: retain license/attribution notices and disclose material modifications if distributed. Verify compliance with internal IP policies and any upstream Voyage AI service terms (if using hosted endpoints).
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model uses trust_remote_code=True in examples, enabling arbitrary Python execution during loading—only load from trusted Hugging Face sources. No published security audit or adversarial robustness testing mentioned. Embeddings are not inherently sensitive, but downstream systems (retrieval, indexing) must validate data access controls. Standard supply-chain practices (verify model hash, use internal caching) recommended.
Alternatives to consider
voyageai/voyage-4-lite
Larger sibling in same family with better retrieval quality, same shared embedding space. Choose if accuracy trumps latency/cost.
sentence-transformers/all-MiniLM-L6-v2
Smaller OSS alternative (22M params) for CPU-only or extreme resource constraints. Lower quality but no custom code required.
nomic-ai/nomic-embed-text-v1
Open-source embedding model with comparable size, supports 32K context, Apache 2.0 licensed. No shared family ecosystem but simpler licensing.
Ship voyage-4-nano with senior software developers
Integrate multilingual embeddings into your RAG or search system today. Load via Hugging Face Transformers, Sentence Transformers, or vLLM. Apache 2.0 licensed for commercial use. Evaluate on your hardware in minutes.
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voyage-4-nano FAQ
Can I use voyage-4-nano in a commercial product?
What GPU do I need to run this?
Can I fine-tune voyage-4-nano on my own data?
What is the context length, and why does the card say 'Unknown'?
Custom software development services
Need help beyond evaluating voyage-4-nano? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Embed? Start with voyage-4-nano
Integrate multilingual embeddings into your RAG or search system today. Load via Hugging Face Transformers, Sentence Transformers, or vLLM. Apache 2.0 licensed for commercial use. Evaluate on your hardware in minutes.