271–280 of 482 posts
Part of its reply was: Quick clarification: As of early 2025, "Qwen 3.6" hasn't been released yet. You are likely looking for Qwen2.5, specifically the Qwen2.5-32B-Instruct model, which is the 30B-class model closest to your 27B reference. The instructions below will use this model.
Weird.
Earlier quoted context omitted.
This one is around 250 t/s prefill and 12.4 generation in my testing.
similar numbers here - slightly higher PP. slightly better peak speed and retention w/ q8_0 kv cache quants too. llama-bench results here, cba to format for hn: https://pastebin.com/raw/zgJeqRbv GTR 9 Pro, "performance" profile in BIOS, GTT instead of GART, Fedora 44
Huh, running the Q4_K_M quant with LM Studio, and asked it "How can I set up Qwen 3.6 27b to use tools and access the local file system?". Part of its reply was: Quick clarification: As of early 2025, "Qwen 3.6" hasn't been released yet. You are likely looking for Qwen2.5, specifically the Qwen2.5-32B-Instruct model, which is the 30B-class model closest to your 27B reference. The instructions below will use this mode…
The pelican is excellent for a 16.8GB quantized local model: https://simonwillison.net/2026/Apr/22/qwen36-27b/ I ran it on an M5 Pro with 128GB of RAM, but it only needs ~20GB of that. I expect it will run OK on a 32GB machine. Performance numbers: Reading: 20 tokens, 0.4s, 54.32 tokens/s Generation: 4,444 tokens, 2min 53s, 25.57 tokens/s I like it better than the pelican I got from Opus 4.7 the other day: https://si…
I feel like this time it is indeed in the training set, because it is too good to be true. Can you run your other tests and see the difference?
The pelican is excellent for a 16.8GB quantized local model: https://simonwillison.net/2026/Apr/22/qwen36-27b/ I ran it on an M5 Pro with 128GB of RAM, but it only needs ~20GB of that. I expect it will run OK on a 32GB machine. Performance numbers: Reading: 20 tokens, 0.4s, 54.32 tokens/s Generation: 4,444 tokens, 2min 53s, 25.57 tokens/s I like it better than the pelican I got from Opus 4.7 the other day: https://si…
I feel like this time it is indeed in the training set, because it is too good to be true. Can you run your other tests and see the difference?
Earlier quoted context omitted.
I just assume any of those companies would steal my work and wouldn't care about it. Everything they have done so far indicates this. Running your own is the only option unless you really trust them or unless you have the option to sue them like some big companies can. Or if you don't really care then you can use the chineese one since it is cheaper. What makes you trust Anthropic more than Alibaba?
There’s a difference between stealing for model training and direct monitoring of actionable trade secrets and corporate espionage. Anthropic and OpenAI wouldn’t do this simply because they would be litigated out of existence and criminally investigated if they did. In China it’s an expected part of the corporate and legal structure with virtually no recourse for a foreign firm and when it’s in states interest domest…
I'm kind of interested in a setup where one buys local hardware specifically to run a crap ton of small-to-medium LLM locally 24/7 at high throughput. These models might now be smart enough to make all kinds of autonomous agent workflows viable at a cheap price, with a good queue prioritization system for queries to fully utilize the hardware.
Need to check out other harnesses for this besides claude code, but the local models are just painfully slow.
Since Gemma 4 came this easter the gap from self hosting models to Claude has decreased sigificantly I think. The gap is still huge it just that local models were extremely non-competitive before easter. So now it seems Qwen 3.6 is another bump up from Gemma 4 which is exciting if it is so. I keep an Opus close ofcourse, because these local models still wander off in the wrong direction and fails. Something Opus almo…