A combination of various renewables (solar, wind, hydro in its various forms), combined with sustainable high density storage and a global network of power delivery with multiple redundancies is the solution.
But it will obviously not happen as it isn’t in shareholders’ interest to have sustainable, cheap energy.
No worries mate, we can’t all be experts of every field and every topic!
Besides there are other AI models that are relatively small and depend on processing power more than RAM. For example there’s a bunch of audio analysis tools that don’t just transcribe information but also diarise it (split it up by speaker), extract emotional metadata (e.g. certain models can detect sarcasm quite well, others spot general emotions like happiness or sadness or anger), and so on. Image categorisation models are also super tiny, though usually you’d want to load them into the DSP-connected NPU of appropriate hardware (e.g. a newer model “smart” CCTV camera would be using a SoC that has NPU to load detection models into, and do the processing for detecting people, cars, animals, etc. onboard instead of on your NVR).
Also by my count, even somewhat larger training systems such as micro wakeword training, would fit into the 196MB V-Cache.
Oh, good to know. Last time I checked around WASM this wasn’t really an option.
AI workflows aren’t limited to LLMs you know.
For example, TTS and STT models are usually small enough (15-30MB) to be loaded directly into V-cache. I was thinking of such small scale local models, especially when you consider AMD’s recent forays into providing a mixed environment runtime for their hardware (GAIA framework that can dynamically run your ML models on CPU, NPU and GPU, all automagically)
And your comment also counts for jackshit since you provided no evidence of your claims, not even your own experience.
So you can fuck right off, buddy.
I have worked on games, and have a good understanding of the workflows involved.
You’ll obviously still need to do the creative parts manually (and should!) but the majority of the work involving the engine core build and the specific game coding, that can all be sped up borderline exponentially.
But I’m glad that someone with absolutely no understanding of the topic does their best to call out those who do show some experience on the topic just because they don’t get a neatly pre-chewed and pre-digested reply detailing all the information they lack and are unwilling to look it up themselves. As a next step would you like me to cut your steak up and feed it to you byte by byte, or tuck you in at night?
See the main issue with that is you need to bundle everything into the app.
Modern computing is inherently cross-dependent on runtimes and shared libraries and whatnot, to save space. Why bundle the same 300MB runtime into five different apps when you can download it once and share it between the apps? Or even better, have a newer, backwards compatible version of the runtime installed and still be able to share it between apps.
With WASM you’re looking at bundling every single dependency, every single runtime, framework and whatnot, in the final binary. Which is fine for one-off small things, but when everything is built that way, you’re sacrificing tons of storage and bandwidth unnecessarily.
Disappointing but not unexpected. Most Chinese companies still work on the “absolute secrecy because competitors might steal our tech” ideology. Which hinders a lot of things…
What, you don’t have a few spare photonic vacuums in your parts drawer?
Well, yeah, when management is made up of dumbasses, you get this. And I’d argue some 90% of all management is absolute waffles when it comes to making good decisions.
AI can and does accelerate workloads if used right. It’s a tool, not a person replacement. You still need someone who can utilise the right models, research the right approaches and so on.
What companies need to realise is that AI accelerating things doesn’t mean you can cut your workforce by 70-90%, and still keep the same deadlines, but that with the same workforce you can deliver things 3-4 times faster. And faster delivery means new products (let it be a new feature or a truly brand new standalone product) have a lower cost basis even though the same amount of people worked on them, and the quicker cadence means quicker idea-to-profits timeline.
It actually makes some sense.
On my 7950X3D setup the main issue was always making sure to pin games to a specific CCD, and AMDs tooling is… quite crap at that aspect. Identifying the right CCD was always problematic for me.
Eliminating this by adding V-Cache to both CCDs so it doesn’t matter which one you pin it to is a good workaround. And IIRC V-Cache also helps certain (local) AI workflows as well, meaning running a game next to such a model won’t cause issues, as both gets its own CCD to run on.
Alright I did read further and damn, you just keep going on being wrong, buddy!
Yes, you can fucking do “stand on the table and make a speech” work. You know how? By breaking it up into detailed steps (pun intended), something that LLMs are awesome at!
For example in this case the LLM could query the position and direction of the table compared to the NPC and do the following:
I’ve asked Perplexity (not even one of the best coding agents out there, it’s mistake ratio is around 5%), and within seconds it spit out a full on script to identify the nearest table or desk, and start talking. You can take a look here. And while my Papyrus is a bit rusty, it does seem correct on even the third read-through - but that’s the fun part, one does not need trust the AI, as this script can be run through a compiler or even a validator (which let’s be honest is a stripped down compiler first stage) to verify it isn’t faulty, which the LLM can then interact with and iterate over the code based on the compiler feedback (which would point out errors).
now mind you this is the output of an internet-enabled, research oriented LLM that hasn’t been fine-tuned for Papyrus and Skyrim. With some work you could probably get a 0.5B local model that does only natural language to Papyrus translation, combined with a 4B LLM that does the context expansion (aka what you see in the Perplexity feed, my simple request being detailed step by step) and reiteration.
You’d also be surprised just how flexible game engines are. Especially freeroaming, RPG style engines. Devs are usually lazy so they don’t want to hardcore all the behaviours, so they create ways to make it simple for game designers to actually code those behaviours and share between units. For example, both a regular object (say, a chair) and a character type object (such as an NPC) will have a move() function that moves them from A to B, but latter will have extra calls in that function that ensure the humanoid character isn’t just sliding to the new position but taking steps as it moves, turns the right direction and so on. Once all these base behaviours are available, it’s super easy to put them together. This is precisely why we have so many high quality Skyrim mods (or in general for Bethesda games).
And again, code quality in LLMs has come a VERY long way. I’m a software engineer by trade, and I’d say somewhere between 80-90% of all the code I write is actually done by AI. I still oversee it, review what it does, direct it the right way when it does something silly, but those aren’t as minor functionalities as we’re talking here. I’ve had AI code a full on display driver for a microcontroller, with very specific restrictions, in about 4 hours (and I’d argue 2 of that was spent with running the driver and evaluating the result manually then identifying the issue and working out a solution with the LLM). In 4 hours I managed to do what otherwise would’ve taken me about a week.
Now imagine that the same thing only needs to do relatively small tasks, not figure out optimal data caching and updating strategies tied to active information delivery to the user with appropriate transformation into UI state holders.
Okay I won’t even read past the first paragraph because you’re so incredibly wrong that it hurts.
First generation LLMs were bad at writing long batches of code, today we’re on the fourth (or by some metric, fifth) generation.
I’ve trained LLM agents on massive codebases that resulted in <0.1% fault ratio on first pass. Besides, tool calling is a thing, but I guess if I started detailing how MCP servers work and how they can be utilised to ensure an LLM agents doesn’t do incorrect calls, you’d come up with another 2-3 year old argument that simply doesn’t have a foot to stand on today.
My own fucking experience. Which I’ve already explained in detail above.
By improving the cadence of projects.
A project costs X amount because of the standard template of pay per time unit Y multiplied by timeframe in time unit Z.
Simply said if you have 100 people working on the project, that costs 100Y per hour. If the project takes 6 months (approx. 960 hours), you multiply the two and get that your costs are 96000Y.
Now the two ways to reduce this is to either reduce the number of employees, with AI you can get rid of maybe 2/3, reducing the expenses to 32000Y…
Or since AI speeds up almost every workflow by about 8 to 10 times, you can keep all the people, but cut down project time from 6 months to about 2 months, which doesn’t just reduce the expenses by the same 2/3 but also increases potential profits for the same 6 month period by 200%, as instead of one product you’re releasing three.
Cutting jobs ain’t the only way to reduce costs with AI.
How do you think most game scripting engines work?
Nowadays game engines don’t rely on strictly speaking hardcoded behaviour, but rather are themselves just a scripting environment to execute a specific format of code.
Skyrim is still the perfect example because it gives you the ability to literally do anything in the world, via a scripting language.
Instructing NPCs to behave in a specific way is also done through these scripts. And LLMs - especially coding fine-tuned ones which could be tied into the execution chain - can easily translate things like <npc paces around> to specific instructions so the NPC walks up and down at a specific distance or in a circle or whatever you want it to do.
You’re seriously over-estimating the work it takes on even crappy, but modern engines to get certain things to happen. Especially when it comes to things that are already dynamically scripted. Like NPCs.
Which is a different article about a (somewhat) unrelated topic.
Using AI for development is already out there, and you can’t put that genie back in the bottle. As an engineer I’m already using it in my daily work for tons of things - I’ve built separate agents to do a number of things:
guess what, AI didn’t replace me, it just allowed me to focus on actually thinking up solutions instead of doing hours of boilerplate stuff.
AI isn’t the enemy in software development. Companies who think they can replace engineers with AI. Middle managers will sooner be on that date, as they were mostly useless anyway.
Yes it is trivial.
LLM can already do tool calling, emotion metadata output and so on. It would take minimal effort for a well tuned model to also output things like facial expressions, body language, hand and body movements and so on.
Sorry but procedural generation will never give you the same result as a well tuned small LLM can.
Also there’s no “hoping”, LLM context preservation and dynamic memory can be easily fine-tuned even on micro models.
Something something ain’t no rest for the wicked