all-MiniLM-L12-v2: Self-Hosting & Deployment Guide
all-MiniLM-L12-v2 is a compact Sentence Transformers model that generates high-quality sentence embeddings for efficient semantic search and similarity tasks.
What is all-MiniLM-L12-v2?
all-MiniLM-L12-v2 is an English sentence embedding model from the Sentence Transformers library designed to map text to dense vector representations. It is mainly used for semantic search, clustering, and information retrieval where fast, approximate meaning-based comparison of texts is required. It is also applied in tasks like duplicate detection, recommendation, and zero-shot text classification via embedding similarity. It belongs to the MiniLM-based family of Sentence Transformers models, which are distilled from larger Transformer architectures to provide lightweight yet effective embeddings.
How to run all-MiniLM-L12-v2 yourself
Open weights (apache-2.0), 33M parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.
all-MiniLM-L12-v2 VRAM requirements
| Precision / quantization | Weights | Figure | Source |
|---|---|---|---|
| BF16 (full precision) | 0.07 GB | Estimate | 33M params × 2 bytes |
File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.
GPU requirements: deploy all-MiniLM-L12-v2 on your hardware
| Hardware | BF16 (full precision) |
|---|---|
| NVIDIA H200141 GB | 1× |
| NVIDIA H10080 GB | 1× |
| NVIDIA A100 80GB80 GB | 1× |
| NVIDIA L40S48 GB | 1× |
GPUs needed per variant.
| Hardware | BF16 (full precision) |
|---|---|
| AWS p5.48xlarge8× H100 · 640 GB | Fits |
| AWS p4de.24xlarge8× A100 80GB · 640 GB | Fits |
| AWS g6e.12xlarge4× L40S · 192 GB | Fits |
| Google Cloud a3-highgpu-8g8× H100 · 640 GB | Fits |
| Azure ND H100 v58× H100 · 640 GB | Fits |
Whole-instance GPU memory; public instance specs.
Not on LLM.API yet
We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).
| Provider | Input | Output | Cache read /M | Latency | Throughput | Uptime |
|---|---|---|---|---|---|---|
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~$0.30 per 1M tokens | $0.00 | — | ~120ms | ~40k tokens/s | ~99.0% |
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~$0.40 per 1M tokens | $0.00 | — | ~200ms | ~20k tokens/s | ~99.5% |
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~$0.10 per 1M tokens | $0.00 | — | ~150ms | ~60k tokens/s | 99.9% |
AWS Bedrock (MiniLM-equivalent Embeddings)
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~$0.12 per 1M tokens | $0.00 | — | ~160ms | ~50k tokens/s | 99.9% |
No hosted provider publishes a price for this exact checkpoint on OpenRouter; self-hosting cost depends on your GPUs. Checked weekly. Last updated 11 Oct 2026.
Try this model
Test all-MiniLM-L12-v2 right here — free to start.
Suggestions for your first prompt
Not available on LLM API yet
We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.
Browse available modelsall-MiniLM-L12-v2: Self-Hosting & Deployment Guide in production
What the model is good at.
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Sentence Embeddings
Generates dense vector embeddings for sentences and short texts, enabling efficient similarity comparison and semantic understanding in downstream applications.
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Semantic Search
Supports semantic search by encoding queries and documents into the same vector space for retrieval based on meaning rather than keywords.
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Text Clustering
Enables clustering of related texts by embedding them into a shared space and grouping vectors according to semantic similarity.
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Multilingual Support
Provides reasonable performance across multiple languages, allowing cross-lingual comparison and retrieval through shared embedding representations.
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Duplicate Detection
Identifies duplicate or near-duplicate sentences and short documents by comparing embedding distances, useful for deduplication tasks.
What you get when you call all-MiniLM-L12-v2: Self-Hosting & Deployment Guide through LLM.API.
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Intelligent Model Routing
Automatically route requests to the best model across providers based on latency, capability, or custom rules—no client changes, just smarter traffic control.
One endpoint, every model -
Cost-Aware Orchestration
Optimize spend by mixing premium and budget models with per-route policies, live price awareness, and guardrails that keep bills predictable at scale.
Maximum output, minimal spend -
Resilient Fallback Logic
Define automatic cross-provider fallbacks when a model fails, degrades, or times out so critical flows stay up without manual incident playbooks.
No single point of failure -
End-to-End Observability
Get unified traces, metrics, and logs for every provider call, with latency, cost, and error insights wired into your existing monitoring stack.
See every token and hop -
Task-Level Abstractions
Describe tasks—chat, tools, search, structured output—once and let LLM.API map them to the right models and capabilities as vendors evolve.
Code to tasks, not vendors -
High-Throughput Batch
Run massive, provider-spanning batch jobs with automatic chunking, retries, and progress tracking, turning offline workloads into a single API call.
Millions of calls, one pipeline
When to Use — When NOT to Use
Use it if...
- You need fast, low-resource sentence embeddings for semantic search or retrieval tasks.
- You need a compact embedding model suitable for deployment on CPUs or edge devices.
- Your use case involves clustering short texts, titles, or sentences into topical groups.
- Your use case involves building lightweight semantic similarity features for traditional ML pipelines.
- You need multilingual-ish robustness for common European languages without strict state-of-the-art accuracy.
- Your use case involves approximate nearest neighbor search over millions of short text entries.
Avoid if...
- You need cutting-edge semantic performance on complex, nuanced queries across many domains.
- Your workload requires strong performance on long documents rather than short sentences.
- You need task-specific embeddings fine-tuned for domain knowledge like legal or medical.
- Your workload requires multilingual coverage beyond primarily English and a few major languages.
- You need embeddings that capture detailed logical structure for advanced reasoning or planning.
- Your workload requires strict robustness to adversarial prompts or security-sensitive embedding use cases.
COMMUNITY
What developers say about all-MiniLM-L12-v2
Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.
- Developers on the Hugging Face forums report that while the model is intended for English, it can return search results for other languages like Polish, though English performance is significantly better.
- Technical experts note that the 12-layer variant serves as a 'small-but-strong' baseline, offering better semantic preservation and robustness against noisy text compared to the faster, 6-layer version.
- Developers indicate that the model may struggle with long documents, specialized technical jargon, or cases where exact token matches for numbers and versions are more critical than general semantic meaning.
SOURCES
Frequently Asked Questions
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What is all-MiniLM-L12-v2?
all-MiniLM-L12-v2 is a lightweight Sentence Transformers model that generates fixed-size sentence embeddings for semantic search, clustering, and similarity tasks.
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What is all-MiniLM-L12-v2 best suited for?
It is best for fast, low-cost semantic search, dense retrieval, and text similarity on short to medium-length English sentences or paragraphs.
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What modalities does all-MiniLM-L12-v2 support via LLM.API?
Via LLM.API, all-MiniLM-L12-v2 supports text-only inputs and returns numerical embedding vectors.
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What context window does all-MiniLM-L12-v2 effectively support?
Although not a generative model, it is typically used on inputs up to a few hundred tokens for reliable sentence embeddings.
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How fast is all-MiniLM-L12-v2 when called through LLM.API?
all-MiniLM-L12-v2 is designed to be very fast, offering low latency for batch embedding generation on CPU and GPU deployments.
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How is pricing for all-MiniLM-L12-v2 handled on LLM.API?
Pricing for all-MiniLM-L12-v2 is determined by LLM.API’s embedding tariff, typically based on the number of tokens or characters processed.
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How do I access all-MiniLM-L12-v2 through LLM.API?
You call the LLM.API embeddings endpoint with the model name "all-MiniLM-L12-v2" and your text input payload.
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How does all-MiniLM-L12-v2 compare to larger Sentence Transformers models?
It trades some embedding quality for significantly smaller size and faster inference compared with larger Sentence Transformers models like mpnet-base.
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What are the main limitations of all-MiniLM-L12-v2?
Its limitations include reduced performance on very long documents, non-English texts, and tasks requiring nuanced world knowledge or reasoning.
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Can all-MiniLM-L12-v2 be used for text generation via LLM.API?
No, all-MiniLM-L12-v2 is an embedding model only and cannot directly generate or complete text.
COMPARE
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