Source: Ollama
The sentence embedding workhorse of the open-source RAG ecosystem. All-MiniLM-L6-v2 and L12-v2 deliver fast, accurate semantic search on CPU hardware — ideal for teams building private document search, knowledge bases, and retrieval pipelines without cloud API dependency.
Source: Ollama
When developers build their first RAG pipeline, they almost universally reach for All-MiniLM-L6-v2. When enterprises audit their production RAG stacks, they frequently find All-MiniLM already running in the embedding layer — often placed there by an engineer who needed something that worked immediately and never got replaced because it kept working well.
This is not an accident. All-MiniLM is the product of Microsoft's sentence-transformers research, distilled from a much larger model down to 22 million parameters without sacrificing the semantic understanding that makes embeddings useful. The result is a model that generates 384-dimensional embedding vectors in milliseconds on CPU hardware, handles the semantic similarity tasks that RAG pipelines depend on with genuine accuracy, and requires so little compute that it can run alongside a generation model on the same modest instance without contention.
For organizations building private document search, internal knowledge bases, customer support retrieval systems, or any RAG-based application where data cannot leave the organization's network, All-MiniLM delivers the embedding layer reliably, efficiently, and at essentially zero marginal infrastructure cost.
6-layer transformer, 22M parameters, 384 dimensions. The standard choice for the vast majority of RAG deployments. Generates embeddings at several hundred documents per second on CPU. Quality is strong for general English semantic search.
ollama pull all-minilm RAM: ~90MB · Dims: 38412-layer variant, 33M parameters, same 384-dimensional output. Approximately 15–20% better on semantic similarity benchmarks at roughly double the inference time. Useful when retrieval quality is the primary constraint.
ollama pull all-minilm:l12 RAM: ~130MB · Dims: 384Which variant to choose: Start with L6-v2. It handles the overwhelming majority of enterprise RAG use cases without compromise. Upgrade to L12-v2 only if retrieval quality testing reveals meaningful gaps on your specific document corpus — the speed difference matters at scale when embedding large document libraries.
This is the question most buyers are actually trying to answer.
Running these models without a proper GPU usually means falling back to CPU only. This works for very light use, but it becomes extremely slow for normal workloads. Many people discover this the hard way after spinning up a cloud server that ends up being too expensive or too slow.
Some teams want to avoid dealing with GPUs altogether. For them, Terminal Glass Cloud is usually the simpler option — you get capable models through a familiar OpenWebUI interface without having to select, size, or manage video cards.
Other teams prefer to run on infrastructure they control. In that case, they typically choose a GPU-based instance (on AWS Lightsail, Digital Ocean, or their own servers) and install NoCloudGPT on it. Because these models are designed to run with local Ollama, you become responsible for selecting and managing the GPU.
Some organizations need everything to stay fully inside their own environment from day one. For those teams, a NoCloudGPT Air Gapped deployment on their own hardware is the right approach.
There isn’t a single right answer. The right answer depends on your constraints around cost, privacy, compliance, latency, and long-term ownership.
All-MiniLM is a great fit if you want to build a personal semantic search tool or a local "chat with my notes" system without needing a powerful GPU. It runs comfortably on everyday laptops and mini PCs, and can index thousands of notes or PDFs in minutes. Keep in mind it only produces embeddings — it does not generate answers on its own. You will pair it with a chat model (such as Llama or Mistral) to complete a RAG pipeline.
For small teams that need a private knowledge base search or internal RAG system, All-MiniLM offers one of the lowest-cost ways to get started. It is lightweight enough to run alongside other workloads on a single modest server, making it practical for validating whether semantic search delivers value before investing in larger models. It performs best on English-language content and smaller to medium-sized corpora.
Developers building RAG prototypes, search features, or recommendation systems will appreciate that All-MiniLM produces compact 384-dimensional vectors that are cheap to store and very fast to query. The small footprint means you can run the embedder in the same container or process as your application without significant resource overhead. It is a reliable default when you prioritize predictable latency and simplicity over maximum retrieval quality.
All-MiniLM remains useful as a fast, well-documented baseline for clustering, topic modeling, deduplication, and similarity studies on English text. It is widely cited and easy to reproduce, making it a sensible reference point when benchmarking newer models such as BGE-M3 or Nomic Embed v1.5. On modern MTEB benchmarks it trails larger models, so it is best positioned as a speed-optimized baseline rather than a state-of-the-art result.
All-MiniLM models (including the widely used all-MiniLM-L6-v2 and all-MiniLM-L12-v2) are released under the Apache 2.0 License, which permits commercial use, modification, and redistribution with attribution. There are no usage-based fees or restrictions tied to the model weights.
Official model card:
Disclaimer: NoCloudGPT provides this licensing summary as a convenience reference only. It does not constitute legal advice. License terms can change. Always review the official model card and consult your legal counsel before deploying any model in a commercial or regulated context.
| Model Size | Ollama Tag | Recommended NoCloudGPT Tier | Notes | |
|---|---|---|---|---|
| MiniLM-L6-v2 (~23M params) | all-minilm:l6-v2 | Starter | The classic fast option. Best for personal RAG, small knowledge bases, and rapid prototyping. Runs well on CPU. | Get quote → |
| MiniLM-L12-v2 (~33M params) | all-minilm:l12-v2 | Starter / Standard | Slightly higher quality than L6-v2 with modest extra cost. A good middle ground when L6 feels too lossy. | Get quote → |
Positioning note: All-MiniLM's main strengths are speed and simplicity. If you need stronger retrieval quality, multilingual support, or longer context handling, consider stepping up to BGE-M3 or Nomic Embed v1.5 instead. Embedding models are lightweight — the main performance bottleneck in a RAG system is almost always the generation model, not the embedder.
L6-v2 can embed hundreds of short documents per second on a standard CPU. For real-time search interfaces and live document ingestion pipelines, this throughput means the embedding layer never becomes the bottleneck.
Trained specifically on sentence-level semantic similarity tasks using contrastive learning. It captures meaning, not just keywords — "quarterly revenue declined" and "sales dropped in Q3" will be close in vector space.
The L6-v2 model weights occupy roughly 90MB of RAM. It runs alongside any generation model on the same instance without meaningful memory competition. Ideal for single-server full-stack RAG deployments.
All-MiniLM is supported natively by LangChain, LlamaIndex, Haystack, ChromaDB, Weaviate, Qdrant, and virtually every RAG framework. Switching vector stores does not require changing the embedding model.
One of the most downloaded embedding models on Hugging Face with millions of production deployments. Edge cases are documented, failure modes are known, and community support for troubleshooting is deep.
At 22M parameters, embedding costs are effectively free in terms of compute. You can re-embed entire document corpora, experiment with chunking strategies, and run embedding-heavy batch pipelines without infrastructure strain.
All-MiniLM is excellent at what it was designed for. It is important to understand where those boundaries are before committing to it as a long-term embedding layer.
256-token input limit. This is the most significant practical constraint. L6-v2 was trained with a 256 subword token maximum, which equates to roughly 180–200 words of English text. Documents longer than this must be chunked before embedding, and the chunk boundary strategy directly affects retrieval quality. For most RAG pipelines this is manageable, but it requires deliberate attention to chunking design.
English-optimized. All-MiniLM performs well on English and adequately on closely related European languages, but it was not designed for multilingual use. Organizations with document corpora in non-European languages should evaluate Paraphrase-Multilingual, BGE-M3, or Qwen3 Embedding instead.
384 dimensions may underfit complex corpora. For large, semantically diverse document libraries — enterprise wikis spanning dozens of domains, research paper corpora, multi-product documentation sets — the 384-dimensional space can become crowded and retrieval precision can suffer. Models like Nomic Embed or BGE-Large at 768 dimensions often show meaningful retrieval improvement on these cases.
All-MiniLM is the right starting point for almost every RAG project. Consider upgrading to a larger embedding model when: (1) retrieval recall testing shows consistent misses on semantically relevant documents, (2) your corpus spans multiple domains with significant vocabulary differences, (3) you need multilingual support, or (4) your documents average longer than 200 words and chunking is creating context fragmentation problems.
| Deployment Scenario | Hardware | RAM Usage | Throughput | Best For |
|---|---|---|---|---|
| CPU — L6-v2 | Any modern server / Lightsail 2GB+ | ~90MB | 300–500 docs/s | All standard RAG deployments |
| CPU — L12-v2 | Any modern server / Lightsail 2GB+ | ~130MB | 150–250 docs/s | Quality-sensitive retrieval |
| Paired with LLM | Lightsail 4–8GB alongside Llama/Mistral | Negligible add | Unaffected by LLM load | Full-stack single-server RAG |
| Batch ingestion | Multi-core CPU server | ~90MB per worker | Linear with cores | Large initial corpus embedding |
All-MiniLM does not benefit meaningfully from GPU acceleration for typical RAG workloads. The model is small enough that CPU inference is fast, and GPU overhead for small batch sizes can actually reduce throughput. Run it on CPU and reserve GPU resources for your generation model.
| Model | Dimensions | Max Tokens | Multilingual | CPU Speed | MTEB Score |
|---|---|---|---|---|---|
| All-MiniLM-L6-v2 | 384 | 256 | Limited | Fastest | 56.3 |
| All-MiniLM-L12-v2 | 384 | 256 | Limited | Fast | 59.8 |
| Nomic Embed v1.5 | 768 | 8192 | Limited | Moderate | 62.4 |
| BGE-Large | 1024 | 512 | Limited | Slow | 63.6 |
| BGE-M3 | 1024 | 8192 | ✓ 100 langs | Slow | 64.8 |
| Mxbai-Embed-Large | 1024 | 512 | Limited | Moderate | 64.7 |
The positioning is clear: All-MiniLM leads on speed and efficiency. Every other model in this table offers better MTEB scores at the cost of more compute, more RAM, and slower inference. For the majority of enterprise RAG deployments, the quality difference does not justify the infrastructure difference. When it does — when retrieval recall is provably insufficient — upgrading to Nomic Embed or Mxbai-Embed-Large is a straightforward model swap.
All-MiniLM configured as the embedding provider for an existing Ollama deployment. Includes Ollama API configuration, vector store connection guidance, and chunking strategy recommendations for your document type.
Complete private RAG deployment: All-MiniLM for embeddings, LangChain or LlamaIndex pipeline, ChromaDB or Qdrant vector store, generation model (Llama, Mistral, or Qwen), and OpenWebUI interface. Document ingestion pipeline included.
Start with All-MiniLM for speed and cost. We architect the vector store and pipeline so upgrading to Nomic Embed or BGE-Large requires only a re-embedding run — no application code changes. Preserves your development investment.
Need longer document context? Nomic Embed Text handles 8K tokens. Need multilingual support? BGE-M3 covers 100 languages. Need the strongest English retrieval? Mxbai-Embed-Large leads MTEB benchmarks. All-MiniLM remains the right default — upgrade only when retrieval testing proves you need to.