The Ultimate Guide to Benchmarking Your Local RAG Setup with Ollama
Z
Zack Saadioui
8/11/2025
The Ultimate Guide to Benchmarking Your Local RAG Setup with Ollama
Hey everyone, so you've dived into the world of local Retrieval-Augmented Generation (RAG) with Ollama. Pretty cool, right? You've got a large language model running on your own machine, hooked up to your own data. The possibilities feel endless. You’ve built a chatbot that can answer questions about your personal documents, or maybe you're prototyping something for your business.
But here's the thing. How do you know if it's actually… any good?
It's easy to get a basic RAG setup running. The internet is flooded with tutorials on how to connect Ollama, LangChain, & a vector database like ChromaDB. But moving from a "it works" prototype to a genuinely useful & reliable application is a whole other ball game. That's where benchmarking comes in, & honestly, it's the part that a lot of people skip.
We're not just talking about whether it's "fast enough." We need to know if the answers it's giving are accurate, relevant, & grounded in the documents you've provided. We need to know if it's making things up (hallucinating), or if it's missing key information. And yes, we also need to know if it's going to crawl to a halt the moment more than one person tries to use it.
This guide is your deep dive into the nitty-gritty of benchmarking a local RAG setup. We'll go beyond the simple "hello world" examples & get into what it REALLY takes to measure, understand, & improve your system.
First, a Quick Refresher: The Local RAG Stack
Just so we're all on the same page, let's quickly break down the moving parts of a typical local RAG setup:
Ollama: This is the star of the show. Ollama makes it incredibly easy to download & run powerful open-source LLMs like Llama 3, Mistral, & others right on your own computer. It handles all the complex setup, giving you a simple server to send prompts to.
The LLM: This is the actual model you're running via Ollama. The model you choose will have a HUGE impact on both performance & quality. A 7B parameter model will be much faster but might not be as nuanced as a 70B model.
LangChain (or similar): This is the orchestration framework. It's the glue that holds everything together. You use it to build the "chain" or pipeline that takes a user's question, retrieves relevant information, & passes it all to the LLM.
Vector Database: This is where your data lives, but in a special format. Tools like ChromaDB, Milvus, or Qdrant take your documents, chop them into chunks, & turn those chunks into numerical representations called embeddings. This allows for super-fast "semantic" search to find the most relevant document chunks for a given question.
The basic flow is simple:
A user asks a question.
LangChain uses the question to search the vector database.
The database returns the most relevant document chunks.
LangChain combines the original question with the retrieved chunks into a new, augmented prompt.
This augmented prompt is sent to the LLM via Ollama.
The LLM generates an answer based on the context you've provided.
Simple on the surface, but the devil is in the details. And you can't improve those details if you aren't measuring them.
Why Bother Benchmarking? Beyond "It Feels Fast Enough"
Benchmarking is about replacing "I think" with "I know." It allows you to:
Objectively Measure Quality: Stop guessing if your answers are good. Put a number to it.
Identify Bottlenecks: Is it the retrieval step that's slow? Or is the LLM itself taking forever to generate a response? You won't know until you measure.
Compare Different Setups: Is
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Llama 3 8B
better than
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Mistral 7B
for your specific use case? Is a bigger chunk size helping or hurting? Benchmarking lets you make data-driven decisions instead of just following the latest hype.
Prevent Regressions: As you tweak your system, you need to make sure you're not accidentally making things worse. A solid benchmarking suite acts as your safety net.
Build Confidence: If you're building a RAG application for others to use, you need to be confident that it's reliable & accurate.
So, let's get into the two main types of benchmarking you need to be doing: Performance Benchmarking & Quality Benchmarking.
Part 1: Performance Benchmarking - The Need for Speed
Performance benchmarking is the more straightforward of the two. It's all about speed & resource usage. A brilliant RAG system that takes 2 minutes to answer a question is… not so brilliant. Here are the key metrics you should be looking at:
Time to First Token (TTFT): This is how long it takes from the moment you send the request to the moment the VERY first word of the answer starts to appear. A low TTFT is crucial for making the system feel responsive. Even if the full answer takes a while, seeing the beginning of it quickly gives the user feedback that something is happening. vLLM, for example, is known for its low TTFT.
Inter-Token Latency (or Tokens per Second): This measures how quickly the rest of the tokens (words) in the answer are generated after the first one. It's often expressed as tokens per second (TPS). A higher TPS means a smoother, faster stream of text. For a user reading along, 10-20 TPS might be fine, but for generating code or long documents, you'll want something much faster.
Total Generation Time: This is the total time taken to generate the full response. It's a combination of TTFT & the time it takes to generate all the subsequent tokens.
Resource Usage: Keep an eye on your CPU, GPU, & RAM usage during the process. Is your GPU memory maxing out? Is your CPU bottlenecking the whole system? This is especially important on local hardware.
Tools for Performance Benchmarking
You don't need to do this with a stopwatch. There are some great tools out there to help you:
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ollama-benchmark
: This is a command-line tool specifically designed to test the throughput of your Ollama models. It's a great starting point for getting a baseline of your raw model performance.
LocalScore: This is a cool open-source tool that benchmarks how fast LLMs run on your specific hardware & then lets you compare your results with others on a leaderboard. It’s built on
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llamafile
for portability and can give you a great sense of how your machine stacks up.
Load Testing tools like LoadForge: If you're thinking of deploying your RAG app for multiple users, you need to see how it performs under pressure. Tools like LoadForge can simulate multiple concurrent users to test the stability & reliability of your local setup.
A quick tip: Your hardware will be the single biggest factor here. Running a 70B model on a laptop with no dedicated GPU is going to be painful. If you're serious about performance, a good NVIDIA GPU is almost a necessity, as it can drastically speed up inference.
Part 2: Quality Benchmarking - The Quest for Good Answers
Okay, so your RAG setup is fast. But is it right? This is where quality benchmarking comes in, & it's a much more complex (but arguably more important) challenge.
The quality of a RAG system depends on two things working in perfect harmony: the Retriever & the Generator. You need to evaluate both.
Open-source frameworks like RAGAS, DeepEval, & TruLens are your best friends here. They provide a suite of metrics to evaluate your pipeline from end-to-end, often using a powerful technique called "LLM-as-a-judge," where another LLM is used to score the quality of your system's output.
Let's break down the key metrics these frameworks use.
Evaluating the Retriever
The goal of the retriever is to find the most relevant document chunks to answer the user's question. If you feed the LLM garbage context, you'll get garbage out.
Context Precision: This metric answers the question: "Of the documents you retrieved, how many were actually relevant?" It's a measure of the signal-to-noise ratio in your retrieved context. A low precision score means you're overwhelming the LLM with irrelevant junk.
Context Recall: This is the flip side of precision. It answers: "Of all the relevant documents that exist in your database, how many did you actually find?" A low recall score means your retriever is missing crucial information, leading to incomplete answers.
Mean Reciprocal Rank (MRR): This is particularly useful when the order of results matters. It measures how high up the list the first correct answer is. A higher MRR means your system is good at putting the best information right at the top.
Evaluating the Generator
Once the retriever has done its job, it's up to the generator (the LLM) to use that context to formulate a good answer.
Faithfulness: This is one of the MOST important RAG metrics. It measures whether the answer generated by the LLM is factually supported by the retrieved context. A low faithfulness score is a big red flag for hallucinations. You want your LLM to stick to the facts you've given it.
Answer Relevancy: This metric checks if the answer is actually relevant to the user's original question. Sometimes the LLM can get sidetracked by the retrieved context & generate an answer that's factually correct according to the context, but doesn't actually answer the question.
Running these evaluations requires a "ground truth" dataset—a set of question-answer pairs that you know are good. You can create this manually, or use some of the advanced features in frameworks like DeepEval to help you generate them.
Part 3: From Benchmarks to Breakthroughs - A Practical Guide to Optimization
So you've run the benchmarks. You have a bunch of numbers. Now what? The whole point of this exercise is to use these insights to make your RAG setup better. Here’s a breakdown of how to optimize each part of your pipeline.
Optimizing the Retriever
If your
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Context Precision
or
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Context Recall
scores are low, your retriever needs some work.
Chunking Strategy: How you split your documents is critical.
Size: Are your chunks too big, containing too much irrelevant noise? Or are they too small, lacking sufficient context? Experiment with different chunk sizes & overlaps.
Technique: Don't just split by a fixed number of characters. Look into more advanced techniques like sentence-window retrieval. This involves retrieving a single sentence that is highly relevant, but then fetching the sentences immediately before & after it to provide more context. You can also build hierarchical indices, where you have summaries of large chunks that point to the more detailed chunks.
Embedding Model: The default embedding model you're using might not be the best for your specific data. Try different ones! Some are better for short text, others for long-form documents.
Hybrid Search: Don't rely solely on semantic (vector) search. Sometimes, good old-fashioned keyword search is better, especially for specific terms or acronyms. The best systems often use a hybrid approach, combining the results of both semantic & keyword search to get the best of both worlds.
Add a Re-ranking Step: A common & VERY effective technique is to have your initial retriever fetch a larger number of documents than you need (say, the top 20) & then use a second, more sophisticated model (a "re-ranker") to re-rank just those top 20 documents to find the absolute best ones to pass to the LLM. This can significantly improve
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Context Precision
.
Optimizing the Generator
If your
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Faithfulness
or
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Answer Relevancy
scores are low, it's time to focus on the LLM & the prompt.
Model Selection: The model matters. A more advanced model might be better at following instructions & sticking to the provided context. Use your benchmarks to compare a few different models from Ollama to see which one performs best for your specific task.
Prompt Engineering: This is a HUGE lever. How you structure the prompt that you send to the LLM can dramatically change the output.
Be Explicit: Your prompt should be crystal clear. Something like:
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"Using ONLY the provided context below, answer the following question. Do not use any outside knowledge. If the answer is not in the context, say 'I do not have enough information to answer that question.'"
This kind of instruction can significantly boost
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Faithfulness
.
Adjust Chunk Order: The order in which you place the retrieved chunks in the prompt can matter. Some studies show that models pay more attention to information at the beginning or end of the context window (the "lost in the middle" problem). Experiment with putting the most relevant chunks first or last.
Optimizing the Overall Pipeline
Sometimes the problem isn't just with one component, but with the way they interact.
Query Transformation: Sometimes the user's question is the problem. It might be too vague or complex. You can add a step at the beginning of your pipeline to use an LLM to reformulate the user's query into a more optimal one for retrieval. Techniques like HyDE (Hypothetical Document Embeddings) involve having the LLM generate a hypothetical answer first, & then using the embedding of that answer to perform the retrieval, which can sometimes be more effective than using the question's embedding.
Caching: If you get the same or similar questions often, cache the results! This can dramatically reduce latency for common queries.
Bringing It All Together for Your Business
Now, imagine you've gone through this whole process. You've benchmarked, you've optimized, & you now have a rock-solid, accurate, & fast local RAG system. This is where things get REALLY exciting for businesses.
You now have the core of an incredibly powerful tool. For instance, you could build an internal knowledge base chatbot that helps employees find information in company documents, with the confidence that the answers are accurate & private.
Or, you could power a customer service bot on your website. This is where a platform like Arsturn comes into play. Arsturn helps businesses create custom AI chatbots trained on their own data. The kind of rigorous benchmarking we've discussed is exactly what's needed to build a customer-facing bot that's not just a gimmick, but a genuinely helpful tool. With Arsturn, you could take your optimized local RAG logic & deploy it as a no-code AI chatbot that provides instant, accurate customer support 24/7, answering questions based on your product documentation or FAQs. It allows you to build those meaningful connections with your audience, because you've done the work to ensure the conversation is personalized & trustworthy.
The privacy benefit of a local RAG setup is a massive selling point for businesses. By keeping the LLM & the data on your own infrastructure, you can build powerful AI solutions without sending sensitive customer or company data to third-party API providers. An optimized & well-benchmarked system gives you the confidence to deploy these solutions in a real-world setting.
Wrapping It Up
Phew, that was a lot. But hopefully, you now see that benchmarking your local RAG setup is not just an optional extra—it's a fundamental part of the development process.
It’s the bridge between a fun weekend project & a production-ready application. It's how you turn a black box into a system you can understand, improve, & trust.
So, grab your favorite local model from Ollama, dust off your RAG pipeline, & start measuring. You might be surprised by what you find.
Hope this was helpful! Let me know what you think.