Mistral AI

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Mistral’s new “environmental audit” shows how much AI is hurting the planet

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Despite concerns over the environmental impacts of AI models, it’s surprisingly hard to find precise, reliable data on the CO2 emissions and water use for many major large language models. French model-maker Mistral is seeking to fix that this week, releasing details from what it calls a first-of-its-kind environmental audit “to quantify the environmental impacts of our LLMs.”

The results, which are broadly in line with estimates from previous scholarly work, suggest the environmental harm of any single AI query is relatively small compared to many other common Internet tasks. But with billions of AI prompts taxing GPUs every year, even those small individual impacts can lead to significant environmental effects in aggregate.

Is AI really destroying the planet?

To generate a life-cycle analysis of its “Large 2” model after just under 18 months of existence, Mistral partnered with sustainability consultancy Carbone 4 and the French Agency for Ecological Transition. Following the French government’s Frugal AI guidelines for measuring overall environmental impact, Mistral says its peer-reviewed study looked at three categories: greenhouse gas (i.e., CO2) emissions, water consumption, and materials consumption (i.e., “the depletion of non-renewable resources,” mostly through wear and tear on AI server GPUs). Mistral’s audit found that the vast majority of CO2 emissions and water consumption (85.5 percent and 91 percent, respectively) occurred during model training and inference, rather than from sources like data center construction and energy used by end-user equipment.

Through its audit, Mistral found that the marginal “inference time” environmental impact of a single average prompt (generating 400 tokens’ worth of text, or about a page’s worth) was relatively minimal: just 1.14 grams of CO2 emitted and 45 milliliters of water consumed. Through its first 18 months of operation, though, the combination of model training and running millions (if not billions) of those prompts led to a significant aggregate impact: 20.4 ktons of CO2 emissions (comparable to 4,500 average internal combustion-engine passenger vehicles operating for a year, according to the Environmental Protection Agency) and the evaporation of 281,000 cubic meters of water (enough to fill about 112 Olympic-sized swimming pools).

The marginal impact of a single Mistral LLM query compared to some other common activities.

The marginal impact of a single Mistral LLM query compared to some other common activities. Credit: Mistral

Comparing Mistral’s environmental impact numbers to those of other common Internet tasks helps put the AI’s environmental impact in context. Mistral points out, for instance, that the incremental CO2 emissions from one of its average LLM queries are equivalent to those of watching 10 seconds of a streaming show in the US (or 55 seconds of the same show in France, where the energy grid is notably cleaner). It’s also equivalent to sitting on a Zoom call for anywhere from four to 27 seconds, according to numbers from the Mozilla Foundation. And spending 10 minutes writing an email that’s read fully by one of its 100 recipients emits as much CO2 as 22.8 Mistral prompts, according to numbers from Carbon Literacy.

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Everybody’s talking about Mistral, an upstart French challenger to OpenAI

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A challenger appears —

“Mixture of experts” Mixtral 8x7B helps open-weights AI punch above its weight class.

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An illustrated robot holding a French flag.

Enlarge / An illustration of a robot holding a French flag, figuratively reflecting the rise of AI in France due to Mistral. It’s hard to draw a picture of an LLM, so a robot will have to do.

On Monday, Mistral AI announced a new AI language model called Mixtral 8x7B, a “mixture of experts” (MoE) model with open weights that reportedly truly matches OpenAI’s GPT-3.5 in performance—an achievement that has been claimed by others in the past but is being taken seriously by AI heavyweights such as OpenAI’s Andrej Karpathy and Jim Fan. That means we’re closer to having a ChatGPT-3.5-level AI assistant that can run freely and locally on our devices, given the right implementation.

Mistral, based in Paris and founded by Arthur Mensch, Guillaume Lample, and Timothée Lacroix, has seen a rapid rise in the AI space recently. It has been quickly raising venture capital to become a sort of French anti-OpenAI, championing smaller models with eye-catching performance. Most notably, Mistral’s models run locally with open weights that can be downloaded and used with fewer restrictions than closed AI models from OpenAI, Anthropic, or Google. (In this context “weights” are the computer files that represent a trained neural network.)

Mixtral 8x7B can process a 32K token context window and works in French, German, Spanish, Italian, and English. It works much like ChatGPT in that it can assist with compositional tasks, analyze data, troubleshoot software, and write programs. Mistral claims that it outperforms Meta’s much larger LLaMA 2 70B (70 billion parameter) large language model and that it matches or exceeds OpenAI’s GPT-3.5 on certain benchmarks, as seen in the chart below.

A chart of Mixtral 8x7B performance vs. LLaMA 2 70B and GPT-3.5, provided by Mistral.

Enlarge / A chart of Mixtral 8x7B performance vs. LLaMA 2 70B and GPT-3.5, provided by Mistral.

Mistral

The speed at which open-weights AI models have caught up with OpenAI’s top offering a year ago has taken many by surprise. Pietro Schirano, the founder of EverArt, wrote on X, “Just incredible. I am running Mistral 8x7B instruct at 27 tokens per second, completely locally thanks to @LMStudioAI. A model that scores better than GPT-3.5, locally. Imagine where we will be 1 year from now.”

LexicaArt founder Sharif Shameem tweeted, “The Mixtral MoE model genuinely feels like an inflection point — a true GPT-3.5 level model that can run at 30 tokens/sec on an M1. Imagine all the products now possible when inference is 100% free and your data stays on your device.” To which Andrej Karpathy replied, “Agree. It feels like the capability / reasoning power has made major strides, lagging behind is more the UI/UX of the whole thing, maybe some tool use finetuning, maybe some RAG databases, etc.”

Mixture of experts

So what does mixture of experts mean? As this excellent Hugging Face guide explains, it refers to a machine-learning model architecture where a gate network routes input data to different specialized neural network components, known as “experts,” for processing. The advantage of this is that it enables more efficient and scalable model training and inference, as only a subset of experts are activated for each input, reducing the computational load compared to monolithic models with equivalent parameter counts.

In layperson’s terms, a MoE is like having a team of specialized workers (the “experts”) in a factory, where a smart system (the “gate network”) decides which worker is best suited to handle each specific task. This setup makes the whole process more efficient and faster, as each task is done by an expert in that area, and not every worker needs to be involved in every task, unlike in a traditional factory where every worker might have to do a bit of everything.

OpenAI has been rumored to use a MoE system with GPT-4, accounting for some of its performance. In the case of Mixtral 8x7B, the name implies that the model is a mixture of eight 7 billion-parameter neural networks, but as Karpathy pointed out in a tweet, the name is slightly misleading because, “it is not all 7B params that are being 8x’d, only the FeedForward blocks in the Transformer are 8x’d, everything else stays the same. Hence also why total number of params is not 56B but only 46.7B.”

Mixtral is not the first “open” mixture of experts model, but it is notable for its relatively small size in parameter count and performance. It’s out now, available on Hugging Face and BitTorrent under the Apache 2.0 license. People have been running it locally using an app called LM Studio. Also, Mistral began offering beta access to an API for three levels of Mistral models on Monday.

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