According to analyst Gartner, small language models (SLMs) offer a potentially cost-effective alternative for generative artificial intelligence (GenAI) development and deployment because they are easier to fine-tune, more efficient to serve and more straightforward to control. In its Explore small language models for specific AI scenarios report, published in August 2024, Gartner explores how the definitions of  “small” and “large” in AI language models have changed and evolved. Gartner notes that there are estimates that GPT-4 (OpenAI – March 2023), Gemini 1.5 (Google – February 2024), Llama 3.1 405B (Meta – July 2024) and Claude 3 Opus (Anthropic – March 2024) have around half a trillion to two trillion parameters. On the opposite end of the spectrum, models such as Mistral 7B (Mistral.AI – September 2023), Phi-3-mini 3.8B and Phi-3-small 7B (Microsoft – April 2024), Llama 3.1 8B (Meta – July 2024) and Gemma 2 9B (Google – June 2024) are estimated to have 10 billion parameters or fewer. Looking at one example of the computational resources used by a small language model compared with those used by a large language model, Gartner reports that Llama 3 8B (eight billion parameters) requires 27.8GB of graphics processing unit (GPU) memory, whereas Llama 3 70B (70 billion parameters) requires 160GB.  The more GPU memory needed, the greater the cost. For instance, at current GPU prices, a server capable of running the complete 670 billion parameter DeepSeek-R1 model in-memory will cost over $100,000.  The fact that a large language model is several times larger than a small language model – in terms of the parameters used during training to build a data model that they use for AI inference – implies that SLMs are only trained on a subset of data. This suggests there are likely to be holes in their knowledge, hence they will sometimes be unable to provide the best answer to a particular query. Distilled SLMs improve response quality and reasoning while using a fraction of the compute of LLMs Jarrod Vawdrey, Domino Data Lab Jarrod Vawdrey, field chief data scientist at Domino Data Lab, an enterprise AI platform provider, notes that SLMs can benefit from a kind of knowledge transfer with LLMs. The technique, known as knowledge distillation (see box below), enables effective transfer from LLMs to SLMs. “This knowledge transfer represents one of the most promising approaches to democratising advanced language capabilities without the computational burden of billion-parameter models,” he says. “Distilled SLMs improve response quality and reasoning while using a fraction of the compute of LLMs.” Vawdrey says knowledge distillation from LLMs to SLMs begins with two key components: a pre-trained LLM that serves as the “teacher”, and a smaller architecture that will become the SLM “student”. The smaller architecture is typically initialised either randomly or with basic pre-training. Neither an LLM nor an SLM alone may deliver everything an organisation needs. Enterprise users will typically want to combine the data held in their corporate IT systems with an AI model.  According to Dominik Tomicevic, CEO of graph database provider Memgraph, context lies at the core of the entire model debate. “For very general, homework-level problems, an LLM works fine, but the moment you need a language-based AI to be truly useful, you have to go with an SLM,” he says. Read more articles about AI models AI models explained: The benefits of open source AI models: In this guide, we explore how to get started with open source AI models and go over how they support your enterprise IT strategy. Latest Alibaba AI model demos AI improvements: The latest model from Chinese public cloud provider Alibaba shows how reinforced learning is driving AI efficiency. For instance, the way a company mixes paint, builds internet of things (IoT) networks or schedules deliveries is unique. “The AI doesn’t need to recall who won the World Cup in 1930,” he adds. “You need it to help you optimise for a particular problem in your corporate domain.” As Tomicevic notes, an SLM can be trained to detect queries about orders in an e-commerce system and, within the supply chain, gain deep knowledge of that specific area – making it far better at answering relevant questions. Another benefit is that for mid-sized and smaller operations, training an SLM is significantly cheaper – considering the cost of GPUs and power – than training an LLM. However, according to Tomicevic, getting supply chain data into a focused small language model is technically a major hurdle. “Until the basic architecture that both LLMs and SLMs share – the transformer – evolves, updating a language model remains difficult,” he says. “These models prefer to be trained in one big batch, absorbing all the data at once and then reasoning only within what they think they know.” This means updating or keeping an SLM fresh, no matter how well-focused it is on the use cases for the business, remains a challenge. “The context window still needs to be fed with relevant information,” he adds. For Tomicevic, this is where an additional element comes in – organisations repeatedly find that a knowledge graph is the best data model to sit alongside a domain-trained SLM, acting as its constant tutor and interpreter. Retrieval augmented generation (RAG) powered by graph technology can bridge structured and unstructured data. Tomicevic says this allows AI systems to retrieve the most relevant insights with lower costs and higher accuracy. “It also enhances reasoning by dynamically fetching data from an up-to-date database, eliminating static storage and ensuring responses are always informed by the latest information,” he says. The resource efficiency of SLMs allows them to run on standard hardware while delivering specialised intelligence exactly where it’s needed, according to Chris Mahl, CEO of enterprise knowledge management platform provider Pryon. “This transforms how organisations deploy AI, bringing powerful capabilities to environments previously considered impractical for advanced computing and democratising access across geographical and infrastructure barriers,” he says. According to Mahl, RAG provides a pipeline that cuts through the noise to deliver precise, relevant context to small language models. While LLMs are regarded as incredibly powerful, they suffer from errors known as hallucinations, whereby they effectively make things up. Rami Luisto, healthcare AI lead data scientist at Digital Workforce, a provider of business automation and technology solutions, says SLMs provide a higher degree of transparency to their inner workings and their outputs. “When explainability and trust are crucial, auditing an SLM can be much simpler compared to trying to extract reasons for an LLM’s behaviour,” he says. While there is a lot of industry hype around the subject of agentic AI, a major barrier to using AI agents to automate complex workflow is that these systems are prone to errors, leading to incorrect decisions being automated. This inaccuracy will improve over time, but there is little evidence that enterprise applications are being developed with tolerance to potential errors introduced by agentic AI systems. In a recent Computer Weekly podcast, Anushree Verma, a director analyst at Gartner, noted that there is a shift towards domain-specific language models and lighter models that can be fine-tuned. Over time, it is likely these smaller AI models will work like experts to complement more general agentic AI systems, which may help to improve accuracy.           • Download this podcast • The analogy is rather like someone who is not a specialist in a particular field asking an expert for advice, a bit like the “phone a friend” lifeline in the TV game show Who wants to be a millionaire? DeepMind CEO Demis Hassabis envisages a world where multiple AI agents coordinate activities to deliver a goal. So, while an SLM may have been transferred knowledge from an LLM through knowledge distillation, thanks to techniques like RAG and its ability to be optimised for a specific domain, the SLM may eventually be called as an expert to help a more general LLM answer a domain-specific question. According to Jarrod Vawdrey, field chief data scientist at Domino Data Lab, the distillation process can be implemented through different methods using both structured data (such as labelled datasets with clear categories) and unstructured data (such as text corpora, conversations, or code): Response-based distillation trains the small language model (SLM) to match the output probability distribution of the large language model (LLM) across a large corpus, focusing on final outputs. Feature-based distillation goes beyond just copying answers – it helps the smaller “student” model learn how the larger “teacher” model thinks by mimicking its reasoning process at different stages. Multi-stage distillation represents a sequential approach where knowledge is transferred through intermediate models of decreasing size. This works like a tutoring system where a college graduate first teaches a bright high school senior, who then simplifies and passes down that knowledge to a younger student. 

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Rusty Rabbit MSRP $20.00 Score Details “Rusty Rabbit needed a tune-up, but there's still some treasure to find in its scraps.” Pros Wacky worldbuilding Surprisingly sharp satire Great drilling concept Cons Movement feels off Weak combat Dull level design Table of Contents Table of Contents A rabbit’s world Surface-level platforming Some games are finely-tuned sports cars. Others are total lemons. Rusty Rabbit is neither; it’s a pile of scraps. Recommended Videos See, scrap ain’t good or bad. That’s what the gruff, yet furry hero of NetEase’s 2D platformer believes, anyways. It’s neutral metal whose value entirely depends on how it’s put together and the skill of the mechanic behind the wrench. If you wheel it into some hack’s garage, you could end up behind the wheel of a rust bucket. Give it to someone who knows what they’re doing and, well, that’s a whole other story. Scrap is unrealized potential, just waiting for the right person to assemble it all into something special. Related Rusty Rabbit doesn’t quite make it out of the junkyard, but all the right pieces are there. It has an inventive world full of junk-loving rabbit, a heartfelt story about parenting, and a central adventure hook that wipes the dust off of Drill Dozer. It’s all just let down by a faulty engine, as its core movement, crafting hook, and combat are all in need of a tune-up from a capable mechanic. Based on a story concept from Gen Urobuchi, Rusty Rabbit is set in a dystopian future in which humanity has long disappeared from Earth. In their absence, rabbits have inherited the world and have evolved to believe they are the planet’s dominant species. They have interpreted the trash and remnants left behind by humans to bend to that narrative, believing that Peter Rabbit is, in fact, the word of God. Religious institutions have arisen, archeology has become a cult, and roving gangs of junksters scour the Smokestack Mountain for precious metals. It’s a fantastic elevator pitch that makes Rusty Rabbit easy to fall in love with on sight, especially once you lay eyes on its fuzzy, grumpy rabbits. Chief among them is Stamp, a middle-aged auto mechanic who likes to live among the junk more than his fellow bunnies. The loner finds himself on a quest to find his long-lost daughter, who has seemingly left behind a trail of data logs in Smokestack Mountain. It all sounds silly at first, but the story has some unexpected heft to it. For one, it’s a story of a parent who has struggled to understand his kid’s world, creating a generational division between them. It’s legitimately moving at times and feels like the product of real parents putting their own insecurities into a deceptively cute video game. It’s a biting work of atheistic satire … More surprising is how Rusty Rabbit deconstructs religion in the context of its own world where the Bible is quite literally a made up storybook taken too literally. It takes religious institutions to task for using unprovable text to build a foundation for a new world order. In one level, I learn that archelogy has been mutated into a religion all its own, where zealots try to claim biomes as holy sites by pointing to a piece of trash they’ve uncovered and interpreted as an object of God. It’s a biting work of atheistic satire that’s often hilarious. Sometimes Rusty Rabbit transcends its wacky elevator pitch, but other times it’s just stuck feeling like a one-note bit. For instance, the funniest meta-joke in the entire game is that Stamp is voiced by Takaya Kuroda in Japanese and Yong Yea in English, Kazuma Kiryu’s respective voice actors in the Like a Dragon series. It’s inspired stunt casting that immediately communicates exactly the kind of guy Stamp is. The buck stops there (at least in the English dub), as Yea delivers every line in an aching slow drawl that gives the adventure’s dialogue-heavy opening a grueling pace. The rest of the voice cast suffers the same problem, dragging out every line read past the point of funny novelty. NetEase Tonal troubles like that persist throughout the entire game, as its presentation is all across the board. It aims to be an unexpectedly mature game wearing a fuzzy pelt, but it doesn’t fully commit to the bit. Sure, the rabbits drop some light cuss words here and there, but it still feels like a kid’s game with preschool music that sounds like it crawled out of a Rugrats episode. The contrast isn’t stark enough to take Rusty Rabbit into full-on Adult Swim irreverence, nor is it suitable as a kid’s game. It’s stuck in the same awkward teenage phase that Detective Pikachu Returns found itself in a few years ago, struggling to tell a story for anxious parents or their angsty children. That imbalance extends to Rusty Rabbit‘s gameplay loops, which seem similarly unsure of themselves. It’s a great premise in theory. Rusty Rabbit is a 2D action-platformer that plays a bit like Drill Dozer. Rusty hops into his trusty mech and hops into biomes filled with dirt blocks and crates he can dig through to find stray scrap, which he can then take back to his home in town to craft new weapons and mech upgrades. It’s a sound idea that takes the satisfying hook of Mr. Driller or Steamworld Dig and spreads it into a more traditional platformer, with levels filled with secrets to unearth. That sturdy idea never quite finds the right structure to support it. It has Metroidvania DNA with traces of gear gated secrets, but the whole thing plays out in fairly linear fashion that doesn’t leave too much flexibility to poke around. It’s also tries to drop a stat-focused RPG on top of that, where Stamp levels up by drilling through blocks and enemies and uses the salvage he’s found to make new, stat-heavy weapons like axes and hammers. I found that I barely needed to engage with that crafting system outside of general weapon upgrades, saving me the hassle of hunting around for extra parts. It wants you to do that, though, as there’s a sort of procedurally generated dungeon to explore back home that’s meant to give players a place to grind for dozens of bolts and screws. That’s where Rusty Rabbit feels most in its element as a roguelike filled with risk-taking exploration, but none of those pieces here click together neatly. The act of moving just feels off, which is the last thing you want from a platformer. What’s worse is that Rusty Rabbit struggles as a straight platformer due to frustrating movement. Stamp’s bot is stiff, unable to maneuver much once I’m in the air. He clings onto nearby walls when he gets close to them, but that magnetism is over tuned. When I’m trying to platform around biomes, I often find myself catching the lip of an edge and getting awkwardly stuck there for a moment before sliding down as I run out of stamina. If you don’t land perfectly flat on a platform, good luck trying to wall jump your way to safety. Falling too far stuns Stamp for a while, kneecapping his ability to maneuver quickly. A missed jump usually results in a good 20 seconds of waiting for the status to clear until players unlock some skill nodes that can reduce the timer. The act of moving just feels off, which is the last thing you want from a platformer. Those mobility issues hurt its already thin combat, too. Stamp can drill, shoot, or slash his way through rust beasts in one-button combat, but enemies have loose hit boxes that often leave weapons passing right through them. When I try to slash an enemy while grounded, Rusty inches forward ever so slightly each time, eventually pushing me into my foe and damaging the mech. Stopping my attack combo to back up a bit, or just jumping my enemy to dodge, means fumbling with clumsy movement that I don’t have much control over. In generic boss fights against giant machines (like tractors that have been mistaken for giraffes), I just wound up eating some damage during my attack combo and topping myself off with healing items rather than slowly resetting my position and dragging the fight out any longer than I had to. NetEase I’d be a little more lenient if there were some clever platforming ideas here that made good use of the core drilling hook, but Rusty Rabbit rarely breaks below the surface. Most biomes just have me drilling my way through paths to find a keycard, unlocking a gate, doing that again a few times, fighting a boss, and moving on. It half-heartedly introduces some spatial reasoning puzzles, like heavy blocks that fall once I dig out the space under them, but it doesn’t change up its exploration much until its final levels. All of that left me frustrated early on, feeling like I’d gotten behind the wheel of a shiny car with a sputtering engine, but I kept digging. And the more I dug, the more I found scraps in the heap that felt salvageable. I got more invested in the world as I went and felt a genuine connection to Rusty as shed more of his stubborn tendencies and accepted how out of touch he was with his kid. He learns that cars aren’t the only thing that need tuning up. We’re all piles of scrap capable of becoming something more so long as we take the care and effort to fix ourselves up. I hope that developer Nitro Plus can take that advice to heart and use it to tighten the screws here, whether that’s in some post-launch tinkering or a sequel rebuilt from the ground up. Treasure only becomes trash once you decide to throw it away. Rusty Rabbit was tested on Nintendo Switch OLED. Editors’ Recommendations

This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now. Have an account? If you feel like you have no idea what anything's supposed to cost anymore, you're not alone. The pandemic and subsequent bout of inflation sent prices all over the place, and now we're facing down whatever is going on with tariffs at any given moment. Unfortunately, our collective state of sticker "Huh?" is likely to get worse. Thanks to a whipsawing economic landscape and the magic of technology, prices can change faster than ever — the price you see in the morning may not be the same that same afternoon. Maybe that new price tag is the result of changing supply and demand dynamics. Maybe it's tariffs. Maybe it's corporate funny business. The unfunny part is that it's impossible for many consumers to know.You might remember the brief dustup around Wendy's and dynamic pricing last year. Its CEO said on an earnings call that the company was going to test out price variations and AI-enabled menu changes, and the internet did a little bit of a freakout. The episode was a sign of the times: Rapid-fire price changes are everywhere. Thanks to digital price tags, QR codes, and the shift to online shopping, it's easier than ever for companies to move prices at the drop of a hat. The pandemic helped firms get more agile at reacting to shocks to the system. Instability represents a prime opportunity to make adjustments, including to a business' benefit."Given this really uncertain, volatile environment, if you're a company who's been thinking of doing dynamic pricing, it could be a good opportunity," said Z. John Zhang, a marketing professor at the University of Pennsylvania's Wharton School of Business who studies pricing strategies and targeting. "This could be a good occasion, a good excuse, an alibi for them to move in that direction."It used to be the case that dynamic pricing — meaning the price of something moving up or down based on market conditions — was largely reserved for flights, hotels, and Ubers. Consumers didn't love the idea their airfare in July was going to run them double what it would in February, but they got it — thus are the rules of supply and demand. But variable pricing is creeping across the economy, including places where it feels a little less understandable. Now, we're in a super-dynamic environment, given the uncertainty emanating from the White House and the global economy. In turn, it's a moment for a super-dynamic approach to prices.Volatility is always a part of business, but now we're facing a "trifecta of volatility," said Craig Zawada, chief visionary officer at Pros Holdings, a price optimization company. First, costs are changing quickly — in the past, a company might negotiate once or twice a year with a supplier, but now they're having to review their agreements all the time. Second, competitive fluctuations are happening. Businesses in the same industry tend to have similar inputs, but not always. If the guy next door imports from China and you don't, you've got an advantage, but if you see them increase their asking price, do you bump yours up a little anyway? Third, consumer demand is shifting, and not always in ways that are obvious or predictable or even bad. In times of economic precarity, sales of items such as lipstick and mini bottles of alcohol tend to go up. We're in a super-dynamic environment, given the uncertainty emanating from the White House and the global economy. In turn, it's a moment for a super-dynamic approach to prices. "All of those dynamics, in this volatile environment, if you're thinking about it as a business, how do you thrive? How do you do better? You need to understand all of those things," Zawada said. "That's where technology comes in. It's much easier now to have visibility on costs, see the reactions to the market, and then respond with prices."Customers recognize how volatile things are and may expect price changes. The willingness to accept these adjustments makes it easier for businesses to do some resets and expand their margins in competitive areas to get more breathing room. This is the type of margin-padding some companies did during this more recent episode of inflation. And if customers reject the increases, businesses can see that, too.Shikha Jain, the lead partner for consumer and retail for North America at Simon-Kucher, a business consultancy, said that determining the price of a good is a lot more than just picking a number."What does it do for consumer demand? How do we balance acquisition and retention? What does that mean for our internal balance sheet and cash flow in terms of our P&L," or profits and losses, she said, "and what does it mean from a competition and market landscape standpoint?"She pointed out that companies have gotten better at figuring all of this out in recent years, especially in the wake of a pandemic that was wildly disruptive to supply chains and consumer behavior across the globe. "We haven't had a stable environment in a long time," she said. Related stories Consumers do not like dynamic pricing, even if it sometimes works to their benefit. Happy hour is dynamic pricing, as are early-bird specials, last-minute flight deals, and flash sales. But to many people, costs constantly moving up and down feels manipulative. They're suspicious it's going to actually work in their favor.It's also the case that dynamic pricing isn't really designed to handle what's happening now. It's usually about supply and demand — that Saturday night taxi being a much hotter commodity than the same one on a Sunday at 2 p.m. — not about whether the president of the United States will jack up the price of everything coming out of China."This is a completely different situation, where you are looking to government policy and how that could change very quickly and how you would change your prices in response," said Eric Greenleaf, a marketing professor at NYU's Stern School of Business who researches pricing.Businesses that sell online can change prices quickly, or just ride the wave of the Amazon algorithm. Some supermarkets and big box stores, such as Walmart, now have digital price stickers that can be adjusted in real time. Consumers are likely to be somewhat understanding that companies aren't having a good time with tariff-by-tweet. One risk businesses face in using slick dynamic pricing tactics, however, is that customers may view it as a little too slick. To blunt any backlash, they may want to pass the buck a bit. So the next time you go to the grocery store to pick up your favorite coffee brand, and it seems pricier than you remember, maybe there's a little asterisk next to the price tag that reads, "*This price increase was brought to you by President Trump, not me." Whereas tariffs can change rather quickly, consumer optimism takes longer to recover. "There'll be a lot of firms, and this is easier online, where they can literally say: 'Here's what we wanted to charge you. Here's the tariff. Here's the total price that you're paying,'" Greenleaf said. "Of course, that will probably upset the Trump administration, but businesses want to make clear that they're not profiting off of this."It's not that different from restaurants slapping on egg surcharges earlier this year. Were wholesale eggs more expensive? Yes. Was it easy for customers to wonder whether they were justifiably 50-cents-an-egg-at-Waffle House more expensive? Also yes. But Zhang, from Wharton, said a tariff surcharge may be a better way to go for businesses than plugging tariffs into whatever pricing algorithm. They can take it off once the tariffs go away, and it's easier to calculate than whatever incremental adjustment happens across the supply chain. Plus, he said, "You can just blame Trump."Not knowing who's to blame is, of course, part of the problem from a consumer perspective. And just because tariffs go away and companies cool it on all the price changes doesn't mean the sour taste it's left in people's mouths will fade so fast."Whereas tariffs can change rather quickly, consumer optimism takes longer to recover," Greenleaf said.Consumers are exhausted. The past five years have been filled with upheaval, and the chaos feels unrelenting. It's understandable people just want their shampoo to cost whatever it costs, no games. But the economy is increasingly gamified, including on pricing, whether or not people want to play.Emily Stewart is a senior correspondent at Business Insider, writing about business and the economy. Thanks for signing up! Look out for your first newsletter with today's big story in your inbox soon. Thanks for signing up! Access your favorite topics in a personalized feed while you're on the go. Related stories

Nature, Published online: 09 April 2025; doi:10.1038/s41586-025-08831-4Absorption and accumulation of atmospheric microplastics by plant leaves occurs widely in the environment.

That second wave of energy is a normal part of the human circadian rhythm, but lifestyle factors also play a role.

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RT Tesla AIGiga Texas production now uses FSD Unsupervised to deliver cars from end of line to the outbound logistics lot. Over 50,000 driverless miles have been accrued between California and Texas factories so far