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Life at the Edge

https://asadk.com/p/edge
1•tosh•5m ago•0 comments

RISC-V Vector Primer

https://github.com/simplex-micro/riscv-vector-primer/blob/main/index.md
2•oxxoxoxooo•9m ago•1 comments

Show HN: Invoxo – Invoicing with automatic EU VAT for cross-border services

2•InvoxoEU•9m ago•0 comments

A Tale of Two Standards, POSIX and Win32 (2005)

https://www.samba.org/samba/news/articles/low_point/tale_two_stds_os2.html
2•goranmoomin•13m ago•0 comments

Ask HN: Is the Downfall of SaaS Started?

3•throwaw12•14m ago•0 comments

Flirt: The Native Backend

https://blog.buenzli.dev/flirt-native-backend/
2•senekor•16m ago•0 comments

OpenAI's Latest Platform Targets Enterprise Customers

https://aibusiness.com/agentic-ai/openai-s-latest-platform-targets-enterprise-customers
1•myk-e•18m ago•0 comments

Goldman Sachs taps Anthropic's Claude to automate accounting, compliance roles

https://www.cnbc.com/2026/02/06/anthropic-goldman-sachs-ai-model-accounting.html
2•myk-e•21m ago•3 comments

Ai.com bought by Crypto.com founder for $70M in biggest-ever website name deal

https://www.ft.com/content/83488628-8dfd-4060-a7b0-71b1bb012785
1•1vuio0pswjnm7•22m ago•1 comments

Big Tech's AI Push Is Costing More Than the Moon Landing

https://www.wsj.com/tech/ai/ai-spending-tech-companies-compared-02b90046
3•1vuio0pswjnm7•24m ago•0 comments

The AI boom is causing shortages everywhere else

https://www.washingtonpost.com/technology/2026/02/07/ai-spending-economy-shortages/
2•1vuio0pswjnm7•25m ago•0 comments

Suno, AI Music, and the Bad Future [video]

https://www.youtube.com/watch?v=U8dcFhF0Dlk
1•askl•27m ago•2 comments

Ask HN: How are researchers using AlphaFold in 2026?

1•jocho12•30m ago•0 comments

Running the "Reflections on Trusting Trust" Compiler

https://spawn-queue.acm.org/doi/10.1145/3786614
1•devooops•35m ago•0 comments

Watermark API – $0.01/image, 10x cheaper than Cloudinary

https://api-production-caa8.up.railway.app/docs
1•lembergs•37m ago•1 comments

Now send your marketing campaigns directly from ChatGPT

https://www.mail-o-mail.com/
1•avallark•40m ago•1 comments

Queueing Theory v2: DORA metrics, queue-of-queues, chi-alpha-beta-sigma notation

https://github.com/joelparkerhenderson/queueing-theory
1•jph•52m ago•0 comments

Show HN: Hibana – choreography-first protocol safety for Rust

https://hibanaworks.dev/
5•o8vm•54m ago•1 comments

Haniri: A live autonomous world where AI agents survive or collapse

https://www.haniri.com
1•donangrey•55m ago•1 comments

GPT-5.3-Codex System Card [pdf]

https://cdn.openai.com/pdf/23eca107-a9b1-4d2c-b156-7deb4fbc697c/GPT-5-3-Codex-System-Card-02.pdf
1•tosh•1h ago•0 comments

Atlas: Manage your database schema as code

https://github.com/ariga/atlas
1•quectophoton•1h ago•0 comments

Geist Pixel

https://vercel.com/blog/introducing-geist-pixel
2•helloplanets•1h ago•0 comments

Show HN: MCP to get latest dependency package and tool versions

https://github.com/MShekow/package-version-check-mcp
1•mshekow•1h ago•0 comments

The better you get at something, the harder it becomes to do

https://seekingtrust.substack.com/p/improving-at-writing-made-me-almost
2•FinnLobsien•1h ago•0 comments

Show HN: WP Float – Archive WordPress blogs to free static hosting

https://wpfloat.netlify.app/
1•zizoulegrande•1h ago•0 comments

Show HN: I Hacked My Family's Meal Planning with an App

https://mealjar.app
1•melvinzammit•1h ago•0 comments

Sony BMG copy protection rootkit scandal

https://en.wikipedia.org/wiki/Sony_BMG_copy_protection_rootkit_scandal
2•basilikum•1h ago•0 comments

The Future of Systems

https://novlabs.ai/mission/
2•tekbog•1h ago•1 comments

NASA now allowing astronauts to bring their smartphones on space missions

https://twitter.com/NASAAdmin/status/2019259382962307393
2•gbugniot•1h ago•0 comments

Claude Code Is the Inflection Point

https://newsletter.semianalysis.com/p/claude-code-is-the-inflection-point
4•throwaw12•1h ago•3 comments
Open in hackernews

Show HN: Run Qwen3-Next-80B on 8GB GPU at 1tok/2s throughput

https://github.com/Mega4alik/ollm
123•anuarsh•4mo ago

Comments

addandsubtract•4mo ago
Great work! Can this technique also be used to run image diffusion models on lower VRAM GPUs?
GTP•4mo ago
Not an expert in machine learning, but AFAIK diffusion models use a completely different architecture, therefore you can't use the same code to run optimized versions of both. But maybe the core ideas can be adapted to diffusion somehow.
anuarsh•4mo ago
Thanks! I don't have much experience with diffusion models, but technically any multi-layer model could benefit from loading weights one by one
cahaya•4mo ago
Nice. Seems like i cannot run this on my Apple silicon M chips right?
jasonjmcghee•4mo ago
Depends how much ram yours has. Get a 4bit quant and it'll fit in ~40-50GB depending on context window.

And it'll run at like 40t/s depending on which one you have

poorman•4mo ago
If you have 64 GB of RAM you should be able to run the 4-bit quantized mlx models, which are specifically for the Apple silicon M chips. https://huggingface.co/collections/mlx-community/qwen3-next-...
cahaya•4mo ago
Got 32GB so was hoping I could use ollm to offload it to my SSD. Slower but making it possible to run bigger models (in emergencies)
tripplyons•4mo ago
I have can host it on my M3 laptop somewhere around 30-40 tokens per second using mlx_lm's server command:

mlx_lm.server --model mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit --trust-remote-code --port 4444

I'm not sure if there is support for Qwen3-Next in any releases yet, but when I set up the python environment I had to install mlx_lm from source.

mhuffman•4mo ago
This particular one may not work on M chips, but the model itself does. I just tested a different sized version of the same model in LM Studio on a Macbook Pro, 64GB M2 Max with 12 cores, just to see.

Prompt: Create a solar system simulation in a single self-contained HTML file.

qwen3-next-80b (MLX format, 44.86 GB), 4bit 42.56 tok/sec , 2523 tokens, 12.79s to first token

- note: looked like ass, simulation broken, didn't work at all.

Then as a comparison for a model with a similar size, I tried GLM.

GLM-4-32B-0414-8bit (MLX format, 36.66 GB), 9.31 tok/sec, 2936 tokens, 4.77s to first token

- note: looked fantastic for a first try, everything worked as expected.

Not a fair comparison 4bit vs 8bit but some data. The tok/sec on Mac is pretty good depending on the models you use.

anuarsh•4mo ago
I haven't tested on Apple machines yet, but gpt-oss and qwen3-next should work I assume. Llama3 versions use cuda specific loading logic for speed boost, so it won't work for sure
mendeza•4mo ago
what is the throughput for gpt-oss, 1 token every 2 seconds is really slow, but understandable because you are moving cache to disk
anuarsh•4mo ago
1tok/2s is the best I got on my PC, thanks to MoE architecture of qwen3-next-80B. gpt-oss-20B is slower because I load all single layer experts to GPU and unpack weights (4bit -> bf16) each time. While with qwen3-next I load only active experts (normally 150 out of 512). Probably I could do the same with gpt-oss.
aappleby•4mo ago
Why even bother with the GPU at that point? CPU would be just as fast if you're bottlenecked on SSD bandwidth.
anuarsh•4mo ago
CPU is much slower than GPU. You can actually use both by offloading some layers to CPU as o.offload_layers_to_cpu(layers_num=12). It is faster to load from RAM than from SSD.
anuarsh•4mo ago
There's one more exciting thing about Qwen3-next (except, efficient MoE architecture and fast linear attention) - MTP (Multi token prediction). It is the additional layer that allows generating more tokens without the need to go through the model again. I'm trying to make it work, but unsuccesfully yet. Maybe someone could help me with it - https://github.com/Mega4alik/ollm/blob/dev/src/ollm/qwen3_ne... (dev brunch). Take a look
ydlr•4mo ago
How dramatically does this shorten lifespan of SSDs?
anuarsh•4mo ago
Good question, need to research this one