A 10 year old Xeon is all you need(point.free) |
A 10 year old Xeon is all you need(point.free) |
For example
E5-2696 v3/v4 E5-2686 v3/v4 E5-2666 v3/v4
also
2673 v3/v4 2678 v3/v4
as well as E5 2629 v3 E5 2649 v3 E5 2669 v3
remember if you serve real customers as a bootstrapped business - you can afford the whole serve down for maintenance. no need for 99.999%.
better than hetzner.
Uh. Uuuh.
No?
___
Also
> While a GPU has a massive pool of ultra-fast High-Bandwidth Memory (HBM), a CPU relies on small, lightning-fast “caches” (L1, L2, L3) built directly onto the processor chip.
What purpose does the quoting of "caches" serve there? Is this AI writing written by that model running on that host?
memory is the bottleneck here (capacity, or rather speed). before you run out to set up your own, try to rather squeeze out the most of your existing hardware. if you are a lucky owner of a lot of cheap memory, you are already in luck. otherwise LM studio allows you to split memory between your gpu and system memory. avoid MoE models or even consider tensor parallelism between the onboard gpu and dedicated one before going for more hardware.
there is little to no benefit for using a specific quantization for your models, so go crazy and test out whatever can easily run for you.
I have no doubt that we will have another wave of cheap retired server gpus just like before. And that is the time when everyone will have their own models at their home.
Or we can just buy the newest medusa halo mini pc. they will be pretty decent, too, albeit pricey.
Model name: Intel(R) Xeon(R) CPU E3-1265L V2 @ 2.50GHz
Product Name: P8Z77 WS
05:00.0 VGA compatible controller: NVIDIA Corporation AD106 [GeForce RTX 4060 Ti 16GB] (rev a1)
05:00.1 Audio device: NVIDIA Corporation AD106M High Definition Audio Controller (rev a1)
This works.
numactl --membind=1
so it is constrained to one of the memory sticks which speeds up token generation a little.
(He has a fully maxed out “last Intel” Mac Pro and laments the lack of replacement).
As it is, the title is click-bait for me, as 1) it says I need at least a Xeon somehow and 2) as it doesn't say what I actually need it for.
Totally just vibes based, I think it goes up to 20+ tps when it's not under load (and that's me trying to be conservative). For context, reading speed at 250 wpm would be around 5 to 6 tokens per second.
https://pcpartpicker.com/products/motherboard/#s=20028,20029...
ik_llama includes llama-sweep-bench https://github.com/ikawrakow/ik_llama.cpp/blob/main/examples...
When comparing hardware, the output of these tools is very helpful to let others put it into context. The post says the output is "reading speed" but knowing the prefill and token generation speeds would be a lot more helpful.
Plus many boards also support CXL for RAM expansion over PCI 5!
Source: building a hybrid inference business for regulated industry workloads.
RAM is really slow at silicon speeds. Very little is reachable in one clock cycle, unless the clock cycle is abysmally slow.
Example. If you have a neuron with 16 inputs each 8 bit wide and with a 4 bit weight per input, you will have 16 specialized multipliers each scaling its input by the corresponding weight and then the 16 scaled inputs feed into an adder tree and finally an activation function.
Are you telling me I should go for it? :)
I do have a dual DGX Spark cluster running MiniMax M2.7 already so I am all for on-prem. But will be interesting how this old machine will perform!
I'd love if anyone knows how I might fare with an old Dell R710 with 2 x Xeon 5600 (12 cores total) and 96Gb of DDR3.
If that is not true, the ME cannot be controlled remotely.
The existence of the ME is much more worrisome in laptops, where the ME can be accessed remotely through WiFi. There, to be certain that there is no way for the ME to be accessed remotely you would have to disconnect or cut the internal antennas and use a USB dongle for WiFi.
As for the second part, I am not sure about how living in a five eyes state would mitigate it. What do you mean by that?
Then there's the current speedup in inference from restricting which subset of the model is used, which is not a "swap in" that would work with hard wired neurons.
But I dunno. Maybe. I'm just guessing.
Try analyzing Epstein files and posting about it, they'll give you a proper penetration test of all your devices to see what you found out about their ex employee.
Nowadays even EU citizens migrating away from US cloud providers are a "national security issue".
I ended up getting a modern 26B MoE model (Gemma 4) running at reading speed on an old recycled server with a single Xeon E5-2620 v4 and 128GB of DDR3 RAM (and no GPU). It took a lot of work, but it actually worked out somehow.
I've also linked the quants at the end, but they're not gonna run unless you use the ik_llama-cpp fork I mention, see other posts for more details.
I'm not an ML engineer, so I'm by no means an expert, and the server is busy acting as a Nix cache, but if you have any question, I can try to answer, but best effort.
But ... isnt that a classic use case for SMT? Giving T1 sth. to do while T0 is waiting on DDR(3) and vise-versa?
I also dont understand the explanation of "--cpu-moe". If an expert has ~ 4.0 GiB of Parameters, why does optimizing the sequence of experts minimize cash trashing? With 20 MiB of L3 Cash vs 4.0 GiB of Parameters, it wont cash any noticeable amount of the Parameters, will it?
As mentioned by others, only some Intel Xeon E5-2xxx v4 did support DDR3, and according to Intel, the E5-2620 v4 is not one of them.
Waiting in terms of latency. When the bus is mostly empty and it takes a while to make a round trip it's great to try to find a few extra passengers to put on it. When the buses are all completely full adding the extra riders just makes the bus stop that much more chaotic.
I wonder if I could get similar or even better performance from similar Dell T7610 workstation with dual Xeons and also 128GB DDR3?
The CPUs are better core wise, but that probably does not make much difference?
It has CPUs 2 × Xeon E5-2697 v2
Cores / threads 24 cores / 48 threads total
Per-CPU cores 12 cores / 24 threads
Base clock 2.70 GHz
Max turbo 3.50 GHz
It is sitting gather dust but reading spead Gemma sounds promising.
You say it runs "at reading speed". Have you benchmarked it?
Noted, and agree (it looks like it has also already been clicked, which I dislike). I honestly I need to redo the themes.
> You say it runs "at reading speed". Have you benchmarked it?
At some point a few weeks ago, yes I think so, but I didn't write it down for some reason... so I'll have to find a time when it's not busy and do it again without a noisy system. Right now the system is noisy, but that said doing it like this:
llama-cli --model gemma-4-26B-A4B-it-Q8_0.gguf --model-draft gemma-4-26B-A4B-t-assistant-GGUF/wikitext-2-raw_ik-llama-mtp_drafter-conservative/gemma-4-26B-A4B-it-assistant-Q8_0.gguf --spec-type mtp --draft-max 3 --draft-p-min 0.0 --color -sm graph -smgs -sas -mea 256 --split-mode-f32 --temp 0.7 --cpu-moe -t 8 --flash-attn on --mla-use 3 --merge-up-gate-experts --special --mlock --run-time-repack --spec-autotune --no-kv-offload --parallel 8 --jinja -p "Why is the sky blue?" -n 128
Gives:
llama_print_timings: load time = 83911.65 ms
llama_print_timings: sample time = 26.99 ms / 128 runs ( 0.21 ms per token, 4742.15 tokens per second)
llama_print_timings: prompt eval time = 343.41 ms / 7 tokens ( 49.06 ms per token, 20.38 tokens per second)
llama_print_timings: eval time = 10639.36 ms / 127 runs ( 83.77 ms per token, 11.94 tokens per second)
llama_print_timings: total time = 11114.98 ms / 134 tokens
When I do it properly, I'll add it to the blog as well!
I just picked up the DDR3 board, an Aliexpress "XD3" so I could reuse some DDR3 ram on a better CPU. Quad channel 1866MT/s is not bad!
E5 CPUs have their supported RAM right on the Intel ARK pages, but short version:
E5-xxxxx v1 and v2 are all DDR3
E5-xxxxx v3 and v4 are all DDR4
Not sure why Intel didn't just cut new model numbers instead of keeping them all as "e5"
More concrete example for E5-2660 (great processor) showing v1 and v2 support DDR3, while v3 and v4, DDR4 (again, different motherboards)
DDR3 v1: https://www.intel.com/content/www/us/en/products/sku/64584/i...
DDR3 v2: https://www.intel.com/content/www/us/en/products/sku/75272/i...
DDR4 v3: https://www.intel.com/content/www/us/en/products/sku/81706/i...
DDR4 v4: https://www.intel.com/content/www/us/en/products/sku/91772/i...
This also means that you need to know the processor your motherboard supports (or, easier, probably RAM) before putting in an order to upgrade the processor. (These processors are incredibly cheap, less than $10 for something that might have cost literally thousands ten years ago, so worthwhile to spend a few minutes and pick out your favorite based on cores, watts, Ghz, etc.)
(Another commenter says that there are some motherboards that accept v3/v4 but also can run slower DDR3 RAM. That's new to me and quite cool - DDR3 is extremely cheap, even now. I did find these motherboards on aliexpress, too: https://www.aliexpress.us/w/wholesale-XD3-motherboard.html?s... and one clearly says v3/v4 cpu's with DDR3 RAM. That could be very useful although memory speeds are slower since CPU performance can be boosted with v3/v4.)
v1: https://www.intel.com/content/www/us/en/ark/products/series/...
v2: https://www.intel.com/content/www/us/en/ark/products/series/...
v3: https://www.intel.com/content/www/us/en/ark/products/series/...
v4: https://www.intel.com/content/www/us/en/ark/products/series/...
search amazon for "chia farming" ...and scroll past chia seeds :)
now same machine is 2.5x the price
https://www.amazon.com/dp/B095TRGCSX
but way cheaper than current ddr5 machines
I have a 3060 12gb card I'd love to hook up to my PoE Reolink cameras for face detection and to get off of the Reolink app.
2.5x?! I have a bunch of older Haswell servers I got for free that are rotting away in my garage. I had initially thought of stripping out the ECC DDR4, but now I'm wondering if I'll get takers on Marketplace...
So either you have a v2 instead of a v4 (and run on DDR3 memory), or you have a v4 but with DDR4 memory (not DDR3)
Everything else doesn't work
Yup that's odd... I've got a Xeon 2680 v4 (14 cores) (amazing bargain of a little beast btw) and it's indeed on DDR4 and I saw all Xeons v4 as supporting DDR4 only.
Full spec (brand/model/mobo type) would have been nice: mine's an HP Z440 workstation repurposed as a server (which I only turn on when I'm working and which I religiously turn off before going to bed).
https://www.intel.com/content/www/us/en/products/sku/92986/i...
CPU(s): 32
On-line CPU(s) list: 0-31
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU E5-2680 0 @ 2.70GHz
This poor thing is currently a YouTube watching box.
I have a dual E5-2667 v2 server with 512GB DDR3 and it's quite nice, the memory bandwidth is higher than of a DDR4 desktop with a way newer CPU, even though it's ECC and registered.
One node's ipmitool sensor report (and self-monitoring PSU, so grain of salt, but my UPS side monitoring tracks closely), reports 250-300w average power use. This though, mind you is for running 22 spinning disks, 2 SAS/SATA SSDs, and 4 NVME ssds, and 768GB of DDR4.
Mid-gen 2015ish Xeons were not great at power reduction, but if you are pegging the cores, they were never particularly slow, and they did have lots of PCIe lanes. This boils down to the CPU/mobo itself not being that big a cost floor, especially if you have high utilization rates.
As a comparison, my main desktop development machine, running a Threadripper 9970X, 128GB of DDR5, a RDNA4 GPU, and a small pile of NVME drives has a power floor of roughly 250W. Some CPU centric workloads you'll definitely lose out on on the older gens of machines, but they are by no means impractical.
Maybe for a desktop usecase they are absolutely suboptimal nowadays, but for a lot of realworld usecases I would say they're still relevant.
---
Like the author posts for the LLM usecase, I think optimizing the hardware choice to the application and not leaving levers unpulled is a big key, especially considering how wide a variety of bandwidth/power draw/peak frequency/corecount SKUs exist in the Xeon lines. Without knowing what you intend to run and fitting the correct processor to it, you will end up with a disappointingly poor environment fit.
As long as performance is useable (apply your own metrics!), pulling it from existing hardware is likely the option with the lower eco footprint.
Also: chances are it'll only be used for this purpose occasionally, and/or for a short while. In that scenario [fabricating new hardware] always has the bigger eco footprint.
EDIT: I stand corrected, 200W is apparently way too high of an estimate. I used to run a bunch of old Xeon servers and they slurped watts like crazy, but I can't remember which ones exactly those were.
There's a lot of budget hosting built around chips like these, and they're suprisingly power efficient.
You likely need to replace the flow-through server chassis system with an active "normal" cooler to achieve a bit of silence.
85W might be about right. My old server CPU is in the same ballpark and compiling kernels it reached about 90w in power usage. If you want to keep it running: idle is not very low power unless you have one of the "low power" L versions, keep that in mind.
[1] https://www.intel.com/content/www/us/en/products/sku/92986/i...
Here's my setup. You may want to figure out what the best optimizations are for your specific CPU like AVX2 because mine didn't have most of them. I did try MTP briefly but I wasn't getting performance improvements. You could play around with the batch sizes for cache or context or go even lower for Q2 and don't overcommit on threads either, but I would suggest either defaults or trying out llama-bench. This isn't by any means the best I assume but it worked decently for me and I sometimes swap out Gemma for Qwen. You could also lower q8_0 to q4_0 for more context but it could hurt quality some say, altough I have noticed it too on some models.
# Building
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=ON -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DGGML_OPENMP=ON
# Running
export OPENBLAS_NUM_THREADS=4
export OMP_NUM_THREADS=4
OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 \
llama.cpp/build/bin/llama-server -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_XL --temp 1.0 --top-p 0.95 --top-k 64 --min-p 0.00 --jinja --host 0.0.0.0 --port 8080 --cache-type-k q8_0 --cache-type-v q8_0 --threads 4 --threads-batch 4 --ctx-size 8192 -n 8192 --batch-size 2048 --ubatch-size 512 --no-mmap --mlock --chat-template-kwargs '{"enable_thinking":false}' --no-mmproj -np 1 -fa 1
Should the market react to the memory shortage, the progress of the Apple silicon continue at the same pace, and what we’ll be able to run locally in 6 years will be very exciting. or frightening.
Also I don’t know what this means for the valuation of the AI companies. I remember asking about this very idea to one of their employees at an event and instead of answering he bailed out to grab a cocktail.
An impressive effort, and better than I would have thought possible on this hardware -- but still pretty far short of what one needs for an satisfactory interactive session.
And it should be just fine for plenty of background use cases.
Details aside, the hope is that ternary LLMs blossom in the coming months and this old hardware can eventually host some very dense models full of factual information, perhaps even larger than the GPU RAM and spilling over to the Optane for IO. Speed would be less important than general factual knowledge. The plan would be to configure then mothball the machine in a Faraday trashcan in the basement, retaining it as a possible "rebuild civilization" oracle should the world fall apart. Of course, power would be an issue in such a scenario, but for how cheap this hardware is and how often AI seems to be practically useful in its latest iterations, why not...
Which makes sense I suppose.
High-Performance AI on a Budget: Optimizing llama.cpp for Qwen3.5 Inference on a Dual-GPU HP Z440
What was the net effect of the optimisations? How much faster did it get?
Guess I am a species-ist after all ;)
e.g. one time I tried making a collaborative drawing application but I messed up the logic, and the brush strokes would just get temporarily mirrored between the client and server, so you'd see it getting drawn over and over again in a loop.
The drawing wasn't stored anywhere, it existed only in the network packets between client and server. Accidental GNU.
http://www.gnuterrypratchett.com/
So I started working on a tool that adds random errors back into my programs. To reintroduce the possibility of such happy little accidents.
It's still a "homelab" beast and does great with development and GIS/Mapping applications. I was not able to figure out how to run AI workloads on it with decent performance, however, so I finally broke down and got a dedicated GPU for it. It's pretty great what can still be done with older hardware.
I use LM studio and qwen3.5 35B - but never figured out if it is swapping or not.
Om am unrelated note, does anyone know a model that can help with this use case:
Would love to see the benchmarks if someone actually pulls something like that off.
So you'd change the invocation slightly here, but a lot of things you can potentially reuse.
That said, the Gemma 4 E4B models have so far in my experience been... not great when it comes to long context, but they are very passable for basic tasks, and even seem surprisingly okay at tool calls.
~/ik_llama.cpp[main]$ build/bin/llama-cli --model ~/models/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled.IQ4_XS.gguf --spec-type mtp --draft-max 3 --draft-p-min 0.0 --spec-autotune -cnv --color --jinja --special -smgs -sas -mea 256 --temp 0.7 -t 6 --parallel 6 --cpu-moe --merge-up-gate-experts --flash-attn on --mla-use 3 --mlock --run-time-repack --no-kv-offload . works pretty fast, at about 15 t/s:
llama_print_timings: sample time = 45.28 ms / 404 runs ( 0.11 ms per token, 8921.67 tokens per second) llama_print_timings: prompt eval time = 949.42 ms / 51 tokens ( 18.62 ms per token, 53.72 tokens per second) llama_print_timings: eval time = 24067.08 ms / 400 runs ( 60.17 ms per token, 16.62 tokens per second) llama_print_timings: total time = 242192.55 ms / 451 tokens
so i wonder why the params used by the quantified qwen model use way less memory than the ones of gemma.
Admittedly web browsers and it don't get along that well. Literally the only thing that drags though on my Slackware 15 system, and even then usually only when it gets to around 15 or so open tabs.
At the low end, I'd use old Xeons with gobs of DDR3, install some V100s, run a smaller agent for general chat inquiries, and a frontier model for the deeper stuff, with a router that passes between them depending on the complexity.
The frontier model would perform very slowly, but if it's a deep task the user can submit it in a batch in the evening e.g. "Correlate all of these cases and look for patterns" then receive the output with morning coffee.
Of course, AI helped me work out a plan for this. Haha
Also I feel like everyone leaves off prompt processing/prefill speeds in these articles. If you are using a very small prompt and asking for mostly generated tokens, sure but I'd love to know the time-to-response of asking for an analysis of an image or a few hundred lines of code.
https://www.techpowerup.com/cpu-specs/ryzen-7-4800u.c2281
It is way too slow
For a long time, too. Programming languages rarely change much, techniques rarely change, so I should be able to use said model for I hope at least five years; and if at any time they optimize local models to cram even more intelligence into the same amount of VRAM, I can upgrade to that.
I like this path.
I experiment with all of the local models I can fit into 32GB of VRAM and I have subscriptions to multiple SOTA providers.
The difference between them is very large, unfortunately. The local models can handle small tasks and refactoring mostly okay, but doing anything challenging with them becomes a waste of time. Unfortunately the waste isn’t immediately obvious because they will come back with something that looks like it works, but then on closer examination I need to throw it out and reset them in a usable direction.
I don’t know OpenAI’s infra, but to the extent they are buying GPUs and building data centers with their own money, that sounds like a bad move.
Satya has mismanaged the AI transition in many ways, but one thing he got right is that models are commodities, and the value is in applications that apply them to create user benefit. I agree that any company trying to build a moat with a model is not long for this world.
For those with tin foil hats, scheme away at possible futures!
Wouldn’t it be in Amazon’s interest to run open models and sell time slots at around the cost of running them?
My only guess for why they don’t is that AI labs are currently selling their models at a huge loss, so this isn’t worth Amazon spending low-margin compute on compared to other higher margin products.
What I’m getting at, is maybe we won’t even need to run the models locally for the current status quo to implode. After today’s AI labs run out of free-money runway and actually have to sell their models at a price above running them, there will be the incentive for anyone with compute to just undercut by selling open-models-as-a-service at commodity prices.
https://docs.aws.amazon.com/bedrock/latest/userguide/model-c...
Paradoxically, the better results we get from general harness of coding agents, the less moat Claude and co. get. It's unbelievably how fast some open models outpaced frontier models of just a few months ago.
I run my word processing software on my apple 2 (a total joke of a computer) instead of running it on the WANG.
I run my book keeping software on visicalc instead of the IBM.
I run my simulation software on my IBM PC (I even paid for the 8087!) instead of the VAX.
Moore's law has, at least so far, allowed the pioneers with toy computers to grow their toys big enough to solve "big boy" problems after some time has allowed the toy computers to be faster and the pioneers have scaled their crappy home-grown solution to solve their 60% of the problem that was originally solved by some enormous complex system.
Eventually the toy infrastructure gets expensive and solves 90-120% of the "big iron" problem space, but it also grows to cost as much as the big iron solution, but then a new generation of toy software and toy systems emerges to disrupt the "big iron" systems.
See also http://www.catb.org/jargon/html/W/wheel-of-reincarnation.htm...
Make the local AI competent enough to do good image generation and editing, realtime voice and music generation, handle agentic tasks with a framework like Hermes, and you can take your AI places to do tasks in contexts that are inaccessible to or inappropriate for cloud.
Frontier big platform models will be the best, but there's a level of "good enough" for local uses that we're already seeing flourish, and "good enough" for the average joe is almost here.
People -- WANT -- this technology on their home devices and (apparently?) the providers of this tech don't seem to be running a profit so they probably don't want the maintenance tail on their side either.
I think it's a bit different. Inevitable that this becomes a household-run thing? Not likely.
The primary feature of "AI" is to process information and reason with a natural language interface at speed, the primary feature of AI bigboys is to provide the machinery that runs the "models".
See the difference?
In my opinion, the bottleneck is the package management layer and not the model capabilities and performance.
I have been an avid Linux user for decades, and if I find it confusing and painful, something is missing.
Sadly - it's going to be ads. Advertising is going to get in there and enshittify the whole thing because as always, advertising income is too easy and too plentiful for any company to resist.
Right now the models are fairly agnostic, but we are a hair-breadth away from ChatGPT responding with, "the right tool for this job is a circular saw - something like the Milwaulkee M18, which happens to be on sale at Home Depot this weekend."
Enough to validate repurposing an existing workstation with enough RAM, or finding a used high VRAM GPU, or in my case buying a Strix Halo system for home lab and local models.
The future is once again not cloud based, for AI tools.
It makes sense to show some ads and get some money at low volume (like a faraway reader wanting to read a story in your local newspaper) but taking money from regular users directly will pay much more.
Newspapers are happy to cannibalize 99% of their ad revenue with a paywall if that 1% subscribes because that’s how much more money you make from someone paying $10-$20/month vs ads.
But yeah, if people use it as a buying recommendation engine, that’s where the money is on ads/referrals but a lot of AI use has little/no connection to buying intent touchpoints.
Most users (potential or actual) are not on a desktop and don't have a beefy discrete GPU. There are "NPU" ASIC chips like what is being put in the new raspberry pi's but their performance and compatibility is not what you might think it is. To get GPU-like performance the ASIC would have to be closer to the size of a real GPU, and at that point why bother. And many devices just don't have the room.
I think you've misunderstood what good enough means in the context - which is a model capable of completing the tasks assigned to it without having the breadth of full generalization. Your analogy breaks down because of this - we did get 'good enough' spec profiles for different hardware. That thing you're wearing on your wrist won't have the same specifications as the box you use to play games.
That's correct. The problem is they have smart people, tons of money, and several years to figure that out, and the best thing they can come up is a coding agent.
That would be the dream... no fucking Electron! No lockdown modules.
I'm shoehorning it back in the Optiplex that donated the ram, so it's not ready to go at the moment, but when I had it running on top of the motherboard box as a test I ran the (9B?) gemma4:e4b-it-q4_K_M since it can fit entirely in the 11gb vram. It flew, more than 50tk/s. A model that small isn't useful for coding, but there could be uses. I'd love to figure out a Wake-on-Use and use it as my personal ChatGPT. I'm not sure how that would work... Maybe proxy the LLM thru a Pi with a script to Wake-on-LAN the PC? It'll be a fun weekend project someday.
My always-on LLM is the dense Gemma4:31b that's not quite half in GPU on a 12gb 2060. It's really slow, but the quality is great and my use case is an automated queue so I'm not sitting there watching the output. I have another 2060 but unfortunately the PC won't POST with both installed for some reason.
if you have an openwrt router this is very easy to do. i have a script on my main working machine that will ssh openwrt and turn on the server and this work well
- There is no "moat" (lasting, easy-to-defend technological edge) in AI model businesses. There are just short-term advantages.
- An AI business is a capital-intensive business, just like old factories. Data centers are expensive, models are energy-hungry, and the hardware inside must be replaced every 3–4 years.
- Smaller, specialized models eat margins from below. Transcription, voice, or image detection do not need large models.
There is no reason to expect high margins like you can in traditional software business. Benefits of AI go mostly to consumers.
edit: There is potential for economies of scale. Few megacorps can strive for cost advantage when they achieve scale (Microsoft, Google, Amazon and Meta)
It does seem like the structural characteristics we’ve observed so far suggest there is a kind of flywheel from short-term to long-term advantage due to the capital requirements at various levels.
If you’re Nvidia, making the best GPUs today, the expanding wavefront of demand is consuming them with volume and margins to give you a huge edge in building out the best next generation of GPUs. Similar to how the mobile wave gave TSMC sustained advantage for about a decade now.
I’m guessing this is also what we’re seeing as Anthropic and OpenAI swap spots in the token-vendor market.
If you get a not-quite-the-best gaming GPU like a 5080, you can run local models that are better than the state of the art from early 2025. Depending on what you want to do, you might have to switch models. The one size fits all huge models are still a data center thing.
Probably nothing. Most users have no idea what an LLM is or how it runs. Anecdotally speaking, I see many LLM users default to whatever their day job provides to them. And even slightly more sophisticated users seem ok with paying for their openai or anthropic subscriptions.
Maybe we will see a small but dedicated group of open weight model users who prefer local llm, but everybody else will just consume from the big providers? The scenario might look something like OS choices today - a small, committed group of Linux users vs the vast majority of other users running Windows, MacOS, or Chrome?
I think there's actually a big market opportunity here. Somebody, like Dell or HP, should start selling turnkey on-prem LLM servers.
A new game is a totally new world with everything created from scratch. A creation. A model, on the other hand, is a reinterpretation machine for hundreds of years of human creations, but not a creation in itself, more like a discovery.
You would think that by now we would have a much better Bitcoin that's taking over the payment networks of the world but what we actually got is a shitload of shitcoin.
I also had no idea RAM and GPU costs would explode they way they did, just happened to do it the right time. I might try to grab a ~$300 3080 on Ebay and sell the 1080ti, but otherwise it's been a great upgrade -- it sucks electricity like Coca Cola, but otherwise performs fantastic as a workstation, and I'm just gonna drive it til the wheels fall off.
> The E5-2620 v4 is great. Have been using it for 10 years now.
Even then, if a commodity chip isn't pushed full tilt at all times, and assuming that the venting and dissipation are adequate, a commodity chip can last a long time.
My i7 920 is still running fine. Or, it was when I decommissioned it in 2017. I don't imagine any reason it shouldn't, except perhaps bitrot of spinning rust (spinning rust rotting is no joke, especially after ~20 years) and maybe aging of thermal paste.
My i7 6950X is still running fine, in use since 2017 even today to write this message.
Most computer equipment fails quickly, otherwise you'll get a long life out of whatever it is.
Given knowable runtime hardware usage patterns (huge bursts of memory bandwidth saturation) and a single limited core/thread-shared resource (memory bandwidth), one could optimize for the constraint ahead of runtime.
Because most of the performance optimization levers you have available to pull are (a) trade compute for memory bandwidth (e.g. compression), (b) preload when memory bandwidth is available, (c) optimize the choice of what's in cache when, (d) align to cache size / memory boundaries.
Or tl;dr, try to approximate GPU ISAs at the CPU compiler level. (Which why would anyone but hobbyists, because everyone else just buys pallets of Nvidia/AMD or designs their own ML chips?)
Well, you can use it for lots of other things as well.
Compared to the cloud you can probably save up to buy a new server every month. And don't underestimate the gains of having something to experiment on and play with.
If a vendor can SaaS a solution, then enterprise is generally happy (they don't want to have to hire folks for maintenance), and that completely locks out any ability to run locally.
Between enterprise's ambivalence and the obvious financial incentive to vendors, you get SaaS-only products.
But my downtimes are a bit self-inflicted: changing ISPs which I can personally workaround but harder for a blog where one expects uptime.
Bearings in fans, caps etc. are also stuff that you need to keep an eye on.
I just replaced a i5-660 thats been powered on since 2010 24/7, heatpaste was fucked so it crashed during heavy loads :)
Except you can overclock v3 :)
I don't run it anymore but my old server was a dual xeon (with two of those coolers crammed in) and I rarely heard a peep out of it.
Hosting a blog 24x7 on a laptop is trivial, except for hyperscaling to the front page of HN and Reddit.
I have seen the results of some early attempts. It fails in such hilarious ways that all these companies are scared of productizing it. But once someone does it, the taboo is broken and everyone else will follow suit immediately.
Here "like" means similarly in magnitude, not direction. If I could predict the future etc.
> a model capable of completing the tasks assigned to it
The thing is, the "task assigned to it" is changing with improved capabilities. If everyone around you in 2036 is using general AI to do amazing stuff, you will probably have little interest in vibe coding slop like it's 2026.
So there is a bigger incentive to run locally something that's gonna get you $20 or $100 worth of bills to OpenAI than to mirror something that is actually free.
Example: In the past there was a whole market for sound cards, if you wanted your computer to have any "multimedia" capabilities you needed to get a sound blaster but now everybody assumes a computer will produce sound, and it's basically for free as all chips have it. Now sound interfaces are still a thing but only for audiophiles who are esoteric enough like me to believe that it's worth to have that extra hi-fi quality.
What I think it could happen, is that eventually AI will be part of all the chips, just like soundcards. And there will be people who will buy specialized AI from companies that perhaps are not OpenAI or Anthropic but second-generation sleepers who watched the carnage in the market and decided to enter when it was reasonable.
This could be Apple, or Nvidia or something new. They're just waiting for the others to do the research and introduce the taste for it to the masses, just like sound blaster made us fall in love with high fidelity sound in our computers.
This is also why the money being poured into datacenters isn't going to result in as much development as you think. It's about leveraging other people's money to lockdown more future hardware. This is going to end exactly like fiber build out in the 2000s. Eventually that fiber got used but the folks who originally paid for it got hosed.
The ‘best’ things are; - fuzzy pattern matching algorithms for traffic analysis, human and other image target recognition.
- targeting algorithms that identify ‘suspicious’ individuals in large volumes of metadata.
- fraud analysis
- antagonistic image and video generation, both for fooling other fraud analysis, but also for propaganda, screwing with other actors, etc.
- directed high speed content generation (text, pictures, video) to spam the ‘algorithm’ and allow near realtime identification of additional buttons to push for given target audiences.
- massive marketing/ad manipulation.
Those budget line items (and the suppliers) really want to stay off the radar however, as it makes their life harder.
Ad/marketing manipulation are exceptionally well done with LLMs in particular.
If you asked someone if drone auto targeting/image recognition or data analysis was ‘AI’, 99% of the time they’ll say yes.
LLMs may or may not be able to cover their costs with it. We'll see - I suspect product placement as recommendations will become a thing as it won't take as much GPU to give a "recommendation" on "the best widget for X". I firmly expect it to become enshittified the same way google and amazon search has.
And that's if LLMs don't become commodified.
2010s Javascript, putting down the controller: Ha, no one will ever surpass my high score for wasting programmer time with dependency churn...
2026 Open Source ML: Hold my beer.
A GPU typically processes close to 1000 tokens/s during eval.
It's probably too small for the timings to be taken seriously.
Hyperscalers can perform this evaluation very quickly because evaluation can be significantly parallelized. The layer `i` output of token `j` only requires access to the layer `i-1` output of all previous tokens, so a parallel frontier develops. Token (0,0) [(token, layer)] is processed first, then tokens (0,1) and (1,0) can be processed in parallel, then (0,2), (1,1), and (2,0), and so on.
The maximum parallel width becomes equal to the number of layers in the model. Gemma 4 26B-A4B model discussed in this article evidently has 30 layers, giving a 30-fold speedup if the system were otherwise unconstrained (all layers can be run in parallel, and one full set of layer outputs is completed in the KV pass for each pass of the parallel sweep).
In the specific output above, however, the input prompt is only seven tokens long so there are probably considerable non-amortized spinup effects at play.
Over 2500 tokens per second on a single request. With 8 MI300X.
If you’ve got something consuming 100 watts average over your 24 hour period, and your electricity costs 20 cents per kWh, you’re already spending almost as much as a Claude subscription.
Just on electricity, this assumes your hardware never fails and you never incur any additional costs.
There’s a big reason why newer more efficient hardware is in demand. Something that’s 10+ years old has drastically worse performance per watt.
Obviously I am not saying to throw away your old hardware as a rule but there is a point where some of this old stuff just isn’t even worth running.
Claude subscription pricing is a broken way to consider footprint.
The test prompt above was "Why is the sky blue?", so there's the seven tokens. I meant to highlight that because I'd expect processing of a thousand-token input to be faster per token than presented.
An Apple M4 chip in a Mac mini has 16MB on the P-cores and 4MB on the E-cores.
Depending on use case, AMD 3D V-cache at almost 100MB could also work out quite well.
So really, if you wait long enough, consumer chips end up with a pretty similar amount of cache.