# AI Landscape

LocoLLM does not exist in isolation. Students and educators have a growing set of options for accessing AI, from free cloud tiers to fully local tools. This document maps the landscape honestly so that users can make informed choices — including the choice not to use LocoLLM.

A project that pretends its alternatives do not exist is selling something. We are not selling anything.

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## Free and Low-Cost Cloud Options

These are the tools most students will encounter first. Many are good. Some are very good.

### Free Tiers

| Provider | Free Offering | Strengths | Limitations |
|----------|--------------|-----------|-------------|
| **Google Gemini** | Generous free tier, education accounts | Excellent capability, large context, multimodal | Rate-limited; depends on Google's pricing decisions |
| **ChatGPT (OpenAI)** | Free GPT-4o-mini access | Strong general capability, familiar interface | Capped usage, reduced features vs paid |
| **Claude (Anthropic)** | Free tier available | Strong reasoning, long context | Usage limits, requires account |
| **Microsoft Copilot** | Free with Microsoft account | Integrated with Office/Edge ecosystem | Quality varies, tied to Microsoft ecosystem |
| **GitHub Copilot** | Free for verified students | Excellent for code | Requires GitHub Education verification |
| **Google Colab** | Free GPU notebooks | GPU access for training/inference | Session limits, queue times, runtime disconnections |

**Honest assessment:** For a student who needs general AI assistance, free Gemini managed carefully is hard to beat as of early 2026. LocoLLM does not pretend otherwise. If a student has reliable internet and their free tier meets their needs, they should use it.

### Cheap API Access

| Service | What It Offers | Approximate Cost |
|---------|---------------|-----------------|
| **OpenRouter** | Aggregated access to many models, some free | Free models available; paid models from $0.10/M tokens |
| **Anthropic Haiku** | Fast, capable, cheap | ~$0.25/M input tokens |
| **GPT-4o-mini** | Strong quality at low cost | ~$0.15/M input tokens |
| **Gemini Flash** | Fast inference, competitive quality | ~$0.075/M input tokens |
| **Groq** | Extremely fast inference | Free tier available; paid from $0.05/M tokens |

**Honest assessment:** For students comfortable with APIs, cheap models like Haiku, GPT-4o-mini, and Gemini Flash are competitive and often superior to any 4B local model on general tasks. The barrier is technical (understanding API keys, endpoints, billing) and financial (even cheap adds up over a semester of heavy use).

### What Is Changing

Free tiers are not guaranteed. As the financial realities of inference compute become clearer:

- Providers are tightening free-tier limits and degrading free-tier model quality
- "Free" often means "free until we have enough users to justify charging"
- Student-specific programmes (GitHub Education, Google for Education) are the most stable but require institutional partnerships
- Free tiers are a user-acquisition strategy, and their long-term availability is not guaranteed

This is not a criticism. It is the economics. But it means that any curriculum built entirely on free cloud AI is one pricing change away from disruption.

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## Local Inference Tools

LocoLLM is not the only way to run models locally. The local AI ecosystem is maturing rapidly, and several tools are excellent.

### Model Runners

| Tool | What It Does | Strengths | Limitations |
|------|-------------|-----------|-------------|
| **Ollama** | Local model serving with simple CLI | Dead simple setup, wide model support, active community | Single model focus, no routing or adapters |
| **LM Studio** | GUI for downloading and running local models | Beautiful interface, model discovery, easy setup | macOS/Windows focus, less scriptable |
| **llama.cpp** | Low-level inference engine | Maximum control, best performance tuning | Requires technical comfort with CLI |
| **Jan** | Local AI assistant with chat interface | Clean UX, offline-first, open source | Newer project, smaller ecosystem |
| **LocalAI** | OpenAI-compatible local API server | Drop-in replacement for OpenAI API, supports many backends | More complex setup, aimed at developers |

### Chat Interfaces

| Tool | What It Does | Strengths | Limitations |
|------|-------------|-----------|-------------|
| **Open WebUI** | Web-based chat interface for local models | Feature-rich, supports Ollama backend, RAG, multi-user | Requires running a separate server |
| **AnythingLLM** | Desktop app for local AI with document ingestion | Good RAG support, workspace concept, multiple LLM backends | Heavier resource usage |
| **Msty** | Lightweight local AI chat | Clean interface, low resource usage, ~5-10 tok/s on CPU | Limited advanced features |
| **GPT4All** | Desktop chat with local models | Simple installation, curated model library | Less flexible than Ollama ecosystem |

**Honest assessment:** A student who installs Ollama and runs Qwen3-4B with a thoughtful system prompt gets roughly 80% of what LocoLLM aims to provide — with dramatically less complexity. If their needs are "brainstorm, draft, iterate, think out loud," that is probably sufficient. Msty deserves particular mention: it provides a polished chat experience with CPU-viable models at 5-10 tokens per second, which is usable for conversation-style interaction.

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## Where LocoLLM Fits

Given all of the above, LocoLLM needs to be clear about what it adds and what it does not.

### What LocoLLM Is Not

- **Not a model runner.** Ollama and llama.cpp handle inference. LocoLLM orchestrates on top of them.
- **Not a chat interface.** Open WebUI and Msty are better pure chat tools. LocoLLM is a system, not a UI.
- **Not a competitor to free-tier frontier models.** On general tasks, free Gemini or ChatGPT will outperform LocoLLM's 4B base. We do not pretend otherwise.
- **Not a product (yet).** It is a teaching and research framework that happens to produce a usable tool.

### What LocoLLM Adds

- **Specialist routing.** No other local tool automatically dispatches queries to domain-specific fine-tuned adapters. Whether this adds meaningful value over a single generalist is an open research question (see [The Router Question](known-challenges.md#the-router-question)), but it is the core differentiator.
- **Systematic evaluation.** Every adapter must prove it beats the base model. No vibes. This evaluation infrastructure does not exist in any of the tools listed above.
- **Inference stacking.** RE2 prompting and self-consistency voting are free on local inference and expensive via API. LocoLLM applies these systematically.
- **A research framework.** The semester-based contribution model, the evaluation gates, the benchmark infrastructure — these produce replicable findings, not just a chatbot.
- **Benchmark data.** Systematic benchmarking of quantized small models on consumer hardware fills a genuine gap in the literature. This data has value independent of whether anyone uses the LocoLLM CLI.

### The Honest Positioning

LocoLLM's value proposition is not "better than the alternatives." It is:

1. **For students:** A local AI tool that works without internet, without rate limits, without cost, and without sending your work to a third party. Use free tiers when they are available. Use LocoLLM when they are not, or when you need unlimited iteration.

2. **For student contributors:** A real engineering project where you learn fine-tuning, evaluation, data curation, routing, and system design by building something that works. The learning is in the building, not in the using.

3. **For researchers:** Benchmark data and methodology for quantized small models that does not exist elsewhere. Routing experiments under hard constraints. Publishable findings regardless of whether the results are positive or negative.

4. **For anyone who cares about dependency:** A small demonstration that useful AI capability does not require a subscription to a company that might change its terms tomorrow. Not a revolution. A proof of concept.

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## Recommendations for Students

This is what LocoLLM actually recommends, not what a marketing page would say:

1. **Start with free tiers.** Google Gemini's free offering is excellent. Use it. Learn prompting, learn to evaluate output critically, develop AI literacy. This costs nothing and teaches the most important skill: working *with* AI, not *for* AI.

2. **Learn about local options.** Install Ollama. Try a small model. Understand what runs on your hardware. This builds technical literacy and gives you a fallback when cloud services are unavailable or rate-limited.

3. **Use cheap APIs when free tiers are insufficient.** Haiku, GPT-4o-mini, and Gemini Flash are remarkable value. If you have a small budget, these stretch further than subscriptions.

4. **Use LocoLLM when it makes sense.** If you need unlimited local inference with no cost anxiety. If you are contributing to the project as a learning exercise. If you care about privacy. If you want to understand how fine-tuning and routing work by building the system yourself.

5. **Do not use LocoLLM because you think you should.** If free Gemini meets your needs, use free Gemini. The goal is student capability, not project adoption.

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## The Bigger Picture

The AI landscape is moving fast. Tools that are cutting-edge today may be commoditised or deprecated within a year. Free tiers may get more generous or may tighten. Local models will get better. New tools will emerge.

LocoLLM's bet is that local capability retains value even as cloud capability improves, for practical reasons:

- **Provider terms change.** Pricing, rate limits, and model availability are not under the user's control. Local inference is.
- **Privacy is structural, not policy-based.** Local inference cannot leak what it does not send.
- **Understanding how AI works** — by building it, not just using it — creates technical skills that using a hosted service does not develop.
- **Offline capability matters** for users with unreliable connectivity or in environments where cloud access is restricted.

Whether that bet pays off is an empirical question. This document exists so that anyone evaluating LocoLLM can see the full landscape and make their own informed decision.
