roosgit

Amazon, the first quadrillion-dollar company.

Can LoRAs be used to increase the quality of these diffusion models? Nvidia mentions something about this https://huggingface.co/nvidia/Nemotron-Labs-Diffusion-8B#inf...

Yeah, it should have been "Datacenter GPUs" or "Nvidia and AMD GPUs".

I just hit that error a few minutes ago. I build my llama.cpp from source because I use CUDA on Linux. So I made the mistake of trying to run Gemma4 on an older version I had and I got the same error. It’s possible brew installs an older version which doens’t support Gemma4 yet.

Have you tried other local models?

The 14B Q4_K_M needs 9GB, but Q3_K_M is 7.3GB. But you also need some room for context. Still, maybe using `--override-tensor` in llama.cpp would get you a 50% improvement over "naively" offloading layers to the GPU. Or possibly GPT-OSS-20B. It's 12.1GB in MXFP4, but it’s a MOE model so only a part of it would need to be on the GPU. On my dedicated 12GB 3060 it runs at 85 t/s, with a smallish context. I've also read on Reddit some claims that Qwen3 4B 2507 might be better than 8B, because Qwen never released a "2507" update for 8B.

I wasn't sure where I'd seen that "retiring" spiel before, but then I remembered someone was (still is) selling a handmade jewelry website claiming $4.3M revenue and $1.3M profit.

I use an even older Macbook and an even older macOS. Of course, the browsers no longer work with the latest JS, so occasionally when I need to use some webapp I boot up a Linux VM and do what I need to do. With limited RAM even that's a pain, but it works for now.

While on the subject, you can make a calendar in as little as 3 lines of CSS: https://calendartricks.com/a-calendar-in-three-lines-of-css/

Can confirm. I was trying to send the newsletter (with SES) and it didn't work. I was thinking my local boto3 was old, but I figured I should check HN just in case.

I have an RTX 3060 with 12GB VRAM. For simpler questions like "how do I change the modified date of a file in Linux", I use Qwen 14B Q4_K_M. It fits entirely in VRAM. If 14B doesn't answer correctly, I switch to Qwen 32B Q3_K_S, which will be slower because it needs to use the RAM. I haven't tried yet the 30B-A3B which I hear is faster and closer to 32B. BTW, I run these models with llama.cpp.

For image generation, Flux and Qwen Image work with ComfyUI. I also use Nunchaku, which improves speed considerably.

# Runs the DB backup script on Thu at 22:00 -- I download the database backup for a few websites that get new data every week. I do this in case my host bans my account.

# Runs the IP change check on Mon - Sun at 09:00, 10:30, 12:00, 20:00 -- If the power goes out or the router reboots I get a new IP. On the server I use fail2ban and if I log into the admin panel I might get banned for making too many requests. So my IP needs to be "blessed".

# Runs Letsencrypt certificate expiry check on Sundays at 11:00 and 18:00 -- I still have a server where I update the certificates by hand.

# Runs the "daily" backup -- Just rsync

# Download Godaddy auction data every day at 19:00 -- I don't actively do this anymore but I used to check, based on certain criteria, for domains that were about to expire.

# Download the sellers.json on the 1st of every month at 19:00 -- I use this to collect data on websites that appear and disappear from the Mediavine and Adthrive sellers.json

I've known about this issue since Lllama 1. Tried it with Llama 2 and Mistral when those models were released. LLMs are not databases.

The test I ran was to ask the LLM about an expired domain of a doctor (obstetrician). I no longer remember the exact domain, but it was similar to annasmithmd.com. One LLM would tell me it used to belong to a doctor named Megan Smith. Another got the name right, Anna Smith, but when I asked it what kind of a doctor, which specialty, it answered pediatrician.

So the LLM had no clue, but from the name of the domain it could infer (I guess that's why they call it inference) that the "md" part was associated with doctors.

By the way, newer LLMs are very good at making domains more human readable by splitting them into words.

I can answer question 3. Prompt processing (how fast your input is parsed) is highly correlated with computing speed. Inference (how fast the LLM answers) is highly correlated with memory bandwidth. So a good CPU might read your question faster, but it will answer pretty much as slow as a cheap CPU with the same RAM.

I have a Ryzen 3 4100. Just tested Qwen2.5-Coder-32B-Instruct-Q3_K_S.gguf with llama.cpp.

CPU-only:

54.08 t/s prompt eval

2.69 t/s inference

---

CPU + 52/65 layers offloaded to GPU (RTX 3060 12GB):

166.79 t/s prompt eval

6.62 t/s inference

Renting could be a good choice to get started. I used to rent a g4dn.xlarge instance from AWS (for Stable Diffusion, not LLMs). More affordable options are Runpod and Vast.ai.

I started with a local system using llama.cpp on CPU alone and for short questions and answers it was OK for me. Because (in 2023) I didn't know if LLMs would be any good, I chose cheap components https://news.ycombinator.com/item?id=40267208.

Since AWS was getting pretty expensive, I also bought an RTX 3060(16GB), an extra 16GB RAM (for a total of 32GB) and a superfast 1TB M.2 SSD. The total cost of the components was around €620.

Here are some basic LLM performance numbers for my system:

https://news.ycombinator.com/item?id=41845936

https://news.ycombinator.com/item?id=42843313

Start with r/LocalLLama and r/StableDiffusion. Look for benchmarks for various GPUs.

I have an RTX 3060(12GB) and 32GB RAM. Just ran Qwen2.5-14B-Instruct-Q4_K_M.gguf in llama.cpp with flash attention enabled and 8K context. I get get 845t/s for prompt processing and 25t/s for generation.

For a while I even ran llama.cpp without a GPU (don't recommend it for diffusion) and with the same model (Qwen2.5 14B) I would get 11t/s for processing and 4t/s for generation. Acceptable for chats with short questions/instructions and answers.

How rich?

You can get some inspiration from businesses for sale on Empire Flippers https://empireflippers.com/marketplace/.

As a rule of thumb for choosing the niche, pick from one of these https://support.google.com/admob/answer/3150953?hl=en

I have a separate PC that I access through SSH. I recently bought a GPU for it, before that I was running it on CPU alone.

- B550MH motherboard

- Ryzen 3 4100 CPU

- 32GB (2x16) RAM cranked up to 3200MHz (prompt generation in memory bound)

- 256GB M.2 NVMe (helps with loading models faster)

- Nvidia 3060 12GB

Software-wise, I use llamafile because on the CPU it's faster by 10-20% for prompt processing than llama.cpp.

Performance "Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf":

CPU-only: 23.47 t/s (processing), 8.73 t/s (generation)

GPU: 941.5 t/s (processing), 29.4 t/s (generation)

I've never used it, but I think Google Colab has a free plan.

As another option, you can rent a machine with a decent GPU on vast.ai. An Nvidia 3090 can be rented for about $0.20/hr.

I think Louie Mantia was an icon designer at Apple back then https://lmnt.me/. Maybe Sebastiaan de With as well https://sdw.space/.

I use it to help me write text.

Don't use any tools. I run it from the command line:

./main -f ~/Desktop/prompts/multishot/llama3-few-shot-prompt-10.txt -m ~/Desktop/models/Meta-Llama-3-8B-Instruct-Q8_0.gguf --temp 0 --color -c 1024 -n -1 --repeat_penalty 1.2 -tb 8 --log-disable 2>/dev/null

I prefer `main` to the new `llama-cli` because when searching history for "llama" I want to get commands that contain the "llama" models, not "mistral" ones, for example.

I had a similar thing happen to one of my websites. In Varnish I used something like this:

if (req.http.host ~ "^(?i)(example.com|www.example.com)") { #redirect to https } else { return(synth(403, "Not allowed.")); }

It basically checks if the host is my domain. I don’t know know what the equivalent of `req.http.host` is on the web server you use. This "solution" might run into issues with Google Translate, but I’m not sure.

I noticed problems with it, as well.

About a year ago I bought some parts to build a Linux PC for testing LLMs with llama.cpp. I paid less than $200 for: a B550MH motherboard, AMD Ryzen 3 4100, 16GB DDR4, 256GB NVMe SSD. I already had an old PC case with a 350W PSU and a 256MB video card because the PC wouldn’t boot without one.

I looked today on Newegg and similar PC components would cost $220-230.

From a performance perspective, I get about 9 tokens/s from mistral-7b-instruct-v0.2.Q4_K_M.gguf with a 1024 context size. This is with overclocked RAM which added 15-20% more speed.

The Mac Mini is probably faster than this. However the custom built PC route gives you the option to add more RAM later on to try bigger models. It also lets you add a decent GPU. Something like a used 3060, as one of comments says.

This is awesome work! But for someone who doesn’t have the necessary skills to pull something like this off, a Samsung Fit Plus USB flash drive might work too. I have one in my old MacBook Air and pretty much don’t notice it. The 128GB version is around $20.

I don’t remember exactly how much it was the first time, but I used to sell WordPress themes (design and coding with full reselling rights) on SitePoint.

I even found the page listing one of the themes I sold with Wayback Machine - "Winning bid: $125". Some people were buying these themes, change the "Theme by" link in the footer and give them away for free. Probably for SEO reasons.

I use /etc/hosts. I made /etc/hosts.default and /etc/hosts.work. At 10:00 `cron` (with sudo) lauches `cp` to copy /etc/hosts.work over /etc/hosts. At 18:00 /etc/hosts.default is copied over.

The /etc/hosts.work file contains a list of websites I don’t want to check during work. But HN is not in this list, yet.

I was under the impression that you have to confirm your email address when you subscribe, but I could be wrong. There’s also an option, as sender, to unsubscribe folks who haven’t open your newsletter in a while.

Another route would be AWS SES. They have a free tier. For managing the subscribe/unsubscribe process you could still use MailChimp or a self-hosted solution like Keila https://www.keila.io/, although I have no experience with this.

I used to use MailChimp. The current free plan seems to be limited to 500 subscribers and 1000 monthly email sends. So if you have less than 250 subscribers you can send a weekly email to all of them, for free.

I noticed they also offer an RSS to Email option, but I couldn’t find if it’s available on the Free plan. https://mailchimp.com/features/rss-to-email/

To me, these features look very unappetizing.

As a teenager I’ve been "traumatized" by Pascal, but I was lucky to find a refuge in HTML and CSS. After I got a job as a web designer I discovered Python and I realized that I could actually program.

The people who came up with this new syntax have different needs that I do and probably have a different kind of brain than mine.

"Trust grows at the speed of a coconut tree and falls at the speed of a coconut."

I remember this from the book Alchemy by Rory Sutherland.