Groq Is the Fastest Inference Engine Available Right Now.
These free tools actually use it.
Here is what each one is worth.

Groq published a benchmark in 2024 that made a lot of developers do a double-take: 750 tokens per second on Llama 3.1 70B, sustained, not burst. For reference, a typical OpenAI API call on GPT-4o lands around 60–80 tokens per second. That gap matters enormously when you are waiting for a 400-line function to be refactored or a stack trace to be explained. Latency is not a luxury concern — it is the difference between staying in flow and losing your train of thought.

This article is a practical inventory of the free tools that actually wire into Groq's Language Processing Unit (LPU) inference infrastructure. Not tools that mention Groq in a blog post. Not tools that might add support someday. The ones you can open a browser tab or clone a repo and use today, without a credit card.

Why Groq's LPU Changes the Coding Assistant Equation

Most AI coding tools are bottlenecked by inference speed, not model quality. The transformer models everyone uses — Llama 3.1, Mixtral 8x7B, Gemma 2 — are already extremely capable for code. The problem is that GPU-based inference, whether on your own RTX 4090 or in a cloud datacenter, involves constant memory bandwidth fights between loading model weights and running the matrix math. Groq solved this differently.

The LPU (Language Processing Unit) is a custom chip where model weights are stored statically in on-chip SRAM rather than fetched dynamically from DRAM. There is no memory bottleneck in the traditional sense. The result is deterministic, extremely fast token generation — closer to a calculator than a GPU cluster scrambling for bandwidth. Groq's published figures for Llama 3.1 70B sit around 500–750 tokens per second per user on their cloud API, which has been independently reproduced by researchers and developers who benchmarked it in 2024.

For coding specifically, this matters in three concrete ways. First, you get complete answers faster, so you can iterate instead of wait. Second, longer outputs — full file rewrites, multi-function refactors — become practical rather than painful. Third, back-and-forth conversation with a model inside a coding session costs you less wall-clock time per exchange, which compounds across a workday.

The models available on Groq's public API include Llama 3.1 8B, Llama 3.1 70B, Llama 3.3 70B, Mixtral 8x7B, and Gemma 2 9B — all open weights, all free to run elsewhere if you want to self-host. Groq's edge is purely execution speed, not proprietary intelligence.

The Groq API Free Tier: The Honest Starting Point

Before you evaluate any tool built on top of Groq, it is worth knowing what the base layer looks like, because every tool in this list is ultimately just a wrapper around the same API endpoint. You can reach it at console.groq.com, sign up with a Google account, and get an API key in about ninety seconds. No credit card required, no trial period that expires.

The free tier gives you access to the full model list with rate limits measured in tokens per minute and requests per day. As of 2025, Llama 3.1 70B on the free tier sits at roughly 6,000 tokens per minute. That sounds like a lot until you are pumping a 2,000-token context through it repeatedly. Still, for most individual developers doing ad-hoc coding help, it is sufficient.

Why does this matter for a "tools" article? Because the right move for many developers is to skip the tool layer entirely and call the API directly. If you already use the OpenAI Python SDK or the openai npm package, Groq's API is wire-compatible. You change one line:

Python: Point base_url to https://api.groq.com/openai/v1 and swap your API key. Everything else — streaming, function calling, system prompts — works identically. This means any script you have written to call GPT-4o can be redirected to Llama 3.1 70B on Groq with a single environment variable change. For benchmarking or experimentation, that is extremely low friction.

The practical limit of using the raw API for coding is that you are writing your own tooling around it: no syntax highlighting in the output, no diff views, no file context management. That is what the tools in the next sections handle.

Open WebUI + Groq: The Best Free Chat Interface for Code

Open WebUI (formerly known as Ollama WebUI) is an open-source, self-hosted chat interface that supports multiple backends including OpenAI-compatible APIs — which is exactly what Groq exposes. You install it via Docker in under five minutes, point it at https://api.groq.com/openai/v1, paste your Groq API key, and you have a polished ChatGPT-style interface running on Llama 3.1 70B at 600+ tokens per second.

For coding work, Open WebUI has a few features that make it genuinely useful rather than just a pretty chat box. Code blocks render with syntax highlighting. You can copy responses to clipboard in one click. Conversation history is stored locally in a SQLite database, so nothing is sent to a third-party server except the actual API call to Groq. The interface supports multiple simultaneous conversations, which lets you keep a "debug this" thread and a "write tests for this" thread open at the same time without context bleeding between them.

The setup cost is Docker familiarity. If you have never run a Docker container, expect to spend twenty minutes learning the basics. If you have, the full setup from zero to working is about eight minutes. The GitHub repo (github.com/open-webui/open-webui) has clear documentation and a one-command Docker run that works on Mac, Linux, and Windows with WSL2.

Where Open WebUI falls short: it does not have native IDE integration, file tree awareness, or the ability to paste a codebase and have it reason about structure. It is a conversation interface. If you want to paste individual functions or files and get targeted help, it works well. If you want something that understands your entire project, you need a different tool.

Compared to using Groq's own GroqChat web interface (chat.groq.com, which is free and requires no setup at all), Open WebUI gives you local storage, model switching, and more control. GroqChat is the faster option if you just want to try Groq once. Open WebUI is the better option if you want it as a daily driver.

Cursor with a Custom Groq Endpoint: IDE Integration Without the Bill

Cursor is a code editor forked from VS Code that has built-in AI features — inline completion, chat, and a composer that can edit multiple files simultaneously. However, Cursor supports "custom API" mode, where you supply your own OpenAI-compatible endpoint and key. Point that at Groq, and you get Cursor's interface and file-context handling with Groq's inference speed, on the free tier.

The setup takes about ten minutes. In Cursor's settings, navigate to Models, add a custom model pointing to https://api.groq.com/openai/v1, name it something like llama-3.1-70b-versatile (Groq's exact model identifier), and add your Groq API key. After that, Cursor's inline chat and composer features will use that model rather than Anthropic or OpenAI.

What you gain: Cursor's editor parses your open files and can include relevant context automatically. When you ask it to fix a bug, it already has the file contents in the prompt. When you ask it to add a function, it knows what imports you have and what style conventions the file uses. That context management is the real value of an IDE-integrated tool versus a standalone chat interface — and you are getting it for free on top of Groq's fast inference.

What you lose: Cursor's most powerful features, the multi-file composer and the long-context codebase indexing, work best with Claude 3.5 Sonnet or GPT-4o. Llama 3.1 70B is very capable but handles extremely long context (100k+ tokens of codebase) less reliably than Anthropic's models. For files under 8,000–10,000 tokens of context, the difference is marginal. For whole-repo reasoning, it matters.

The free Cursor tier (Hobby) gives you 2,000 completions per month using Cursor's own model budget. Using a custom Groq endpoint bypasses that limit entirely — you are spending your own Groq API quota, not Cursor's. This is the most practical trick for developers who want IDE integration without paying for Cursor Pro.

Continue.dev: The Open-Source VS Code Extension That Works With Groq

Continue (continue.dev) is an open-source VS Code and JetBrains extension that lets you wire any LLM backend into your IDE. It is not a Groq-specific product, but it has first-class support for OpenAI-compatible APIs, which means Groq integration is four lines in a JSON config file.

The config looks like this: set provider to groq (Continue added a native Groq provider in late 2024), set your apiKey, pick a model like llama-3.1-70b-versatile, and you have inline code suggestions, a chat panel that can read your open files, and slash commands like /edit and /test. The entire extension is MIT-licensed and the source is on GitHub.

Continue's strongest feature for coding is its context management system. You can manually @-mention files, functions, or even documentation URLs, and Continue will include them in the prompt. You can type @src/auth.py in the chat and it pastes the relevant file contents into the context automatically. Combined with Groq's throughput, this produces noticeably faster turnaround than the equivalent workflow in GitHub Copilot's chat panel, which runs on OpenAI's servers at standard GPU speeds.

The limitation worth knowing: Continue's autocomplete feature (the grey ghost-text suggestions as you type) works best with smaller, faster models — Groq's Llama 3.1 8B is a reasonable choice there, because autocomplete latency needs to be under 300ms to feel natural. The 70B model on Groq is fast, but not always fast enough for keystroke-level completion. For chat and on-demand help, use 70B. For inline ghost-text, use 8B or consider a locally-run small model via Ollama as a fallback.

Continue also supports codebase indexing, where it embeds your project files and retrieves relevant chunks into context. That indexing uses whatever embedding model you configure — you can use Groq for generation and a local embedding model (like nomic-embed-text via Ollama) for retrieval, keeping everything free.

GroqChat and the Groq Playground: Zero-Setup Options Worth Knowing

If you want to try Groq's speed without installing anything, two browser-based options exist: GroqChat (chat.groq.com) and the Groq Playground (console.groq.com/playground). Both are free, both require only an account, and both give you direct access to the full model list.

GroqChat is the consumer-facing interface. It is Groq's own equivalent of ChatGPT — a clean chat UI that defaults to Llama 3.1 70B. For coding questions, it works well: paste a stack trace, ask for a function, get a response in under two seconds. It has no file upload (as of mid-2025), no conversation export, and no system prompt customization from the UI. It is the fastest way to get a quick answer. It is not a workflow tool.

The Groq Playground is aimed at developers evaluating the API. It shows you the raw request and response, lets you adjust temperature, max tokens, and system prompts, and displays the token generation speed for each response. That last feature is actually useful — you can see exactly how fast the model runs on your specific prompt type. If you are deciding which model to use in your application, the Playground lets you A/B between Llama 3.1 8B, 70B, and Mixtral in real time and compare both quality and speed.

Neither tool gives you IDE integration or file context management. They are best treated as a testing environment and a quick-reference chat, not as a daily coding assistant. But for someone evaluating Groq before committing to a setup — trying it for five minutes in GroqChat is the right first step.

Which Tool to Actually Use, Based on Your Situation

The honest answer here is not "use all of them." Pick one and get good at it, because the marginal gain from switching tools constantly is negative.

If you work primarily in VS Code or a JetBrains IDE: Install Continue.dev, configure it with Groq as the backend, and use Llama 3.1 70B for chat and 8B for autocomplete. This is the highest-leverage setup because your editor already has the context of what you are working on. The setup takes twenty minutes and then runs silently.

If you already use Cursor and do not want to pay for Pro: Add a custom Groq endpoint in Cursor's model settings. You get Cursor's UX — which is genuinely excellent — with Groq's speed and your own free API quota. The tradeoff is that very long context tasks (whole-repo refactors) will be less reliable than they would be with Claude 3.5 Sonnet. For most day-to-day coding tasks, that tradeoff is acceptable.

If you want a standalone chat interface outside your IDE: Open WebUI running locally against Groq is the best option. It stores your history locally, gives you a clean interface, and lets you switch models easily. GroqChat is the right choice if you do not want to run Docker and just need something that works.

If you are building something on top of Groq: Skip the tools and call the API directly. The OpenAI SDK compatibility means zero migration cost from any existing OpenAI integration. Use the Playground to calibrate which model fits your use case before writing production code.

One thing worth being direct about: none of these free tools match the full experience of a paid service like GitHub Copilot Enterprise, which has deep IDE integration, pull request summarization, and repository-level context at scale. The free Groq stack is excellent for individual developers, students, and teams doing focused coding tasks. It is not a wholesale replacement for enterprise tooling. But for the price — zero — it is remarkably good.