I keep finding more and more usecases for Q3.6 27b (same league) and the best performance is, when answers to my question is already in the context.

The moment I'm trying something open-ended or ambitious, Claude/ChatGPT clearly take you to the goal quicker.

For things, where there's a way to build a knowledgebase though, the local llm definitely can be a true contender. Plus, having a big context and no worries about filling it over and over - you can get quite far.

I'm writing this, literally in between cooking a pasta, that the local llm ordered products for me online. I've built a grocery shopping skill, so that it roughly knows what I have in fridge (losely), my last 10 representative orders (general preferences plus rich info about shops and skus around me) and actual real-time in stock info. The last part has been my personal pet peeve for every product that promised cooking ingredient delivery (that is not packaged specifically for that).

This is what has been promised to us by every big tech company with an agent, and now a local llms actually solved that for me fully.

It's also going to fail consistently. When calling Claude you don't know what version of the model you are talking to, it might be quantified sure to load or have been patched.

This is true. The failure modes are simpler. And yes the ceiling is lower as well. Smaller models stability is lower over long sequences. And thus anything that needs a lot of CoT will be weaker. For example, I had a dumb lock + condvar with multiple defenses against lost wakeups in a N producer 1 consumer queue thing. Models generally need a lot of CoT before they realise they can switch it to a semaphore instead. Qwen typically isn't stable over such long CoTs and ends up adding more and more slop and band aids versus a larger model that outputs a large CoT and then realises it can swap 3 functions out with 2 lines if we use a semaphore.

That’s the cost of using a new hardware provider. A single RTX Pro 6000 Blackwell Max-Q will do better than that and be much more usable. I have 2 running DS4 Flash at 160 tok/s with max num seqs 4.

Very interesting though, these Tenstorrent chips. Might get one to experiment with.

How is the software compatibilty with the Tenstorrent cards? Are you stuck using vendor supplied runtimes/models?

It's surprising how little these things come up given the price they go for

> I pay ~3$ per 1M/tokens for that model on Openrouter

I think the thing is, there's an unspoken "for now" at the end of that sentence and people running this locally are hedging against that "for now". Some people prefer to feel that they own the means rather than rent the means, even if the one they own is worse than the one they can rent. Especially with today's Fable news and the harsh realisation that the "for now" is dependent on very many unpredictable factors, where the one you have locally costs you capital today and a relatively predictable run-rate (made more predictable with on-prem solar for example), but should otherwise work predictably forever.

I'm not saying that you're wrong to do what you're doing, just that many people have their own lines in the sand where renting vs buying makes sense, and it doesn't only boil down to a rational (or irrational) financial decision.

I've spent the past week trying to scheme a way to get affordable local inference for ~$500 and have come to the conclusion that it simply isn't viable. A pair of power-restricted P100s in a workstation gets close but the workstations themselves are expensive and rare as hen's teeth (not to mention loud and large). I think early '27 will be when things open up as the hardware market unclenches and further strides are made in small capable models.

An R9700 is $1350 and can get 100 TPS running Qwen3.6-35B-A3B Q5 with 130k context window (with room to spare) with a bit of fine tuning llamacpp-vulkan, but llamacpp's repository instability and lack of real versioning frustrates me.

In terms of electricity, if you aren't using it, even with all the vram loaded, at most your wasting about 30 watts or so.

Prompt processing a large uncached context is annoying, which is why I forced a lower context window, but I don't know if it's any worse in performance than the cloud models I've used.

There's a niceness, to me, knowing I don't have to rent it anymore. If you rent it, the terms can change regularly.

I use local models to explore, hosted models to refine. I somewhat envy those who can sustain local models (q8 120b+) running as a hobby.... for me, the practical path is a better SearXNG setup and knowing my routes forward.

When they declare open models a 'security risk', his setup will be running, yours will not and even that 3090 will be way outside of your reach.

It’s a personal hobby project why should we care this is how someone chooses to spend their free time and money? Lots of hobbies are expensive and pointless if you think of commercially available offerings. That’s why it’s a hobby and not a small business

You are paying with your privacy ...

Yeah but they can also be used to play games and do other stuff.

Rtx 3090 24 gb set me back 390€ a year ago ( 2nd hand)

Openrouter doesn't give you access to the models internals, i.e. complete control of logprobs, sampler stack, any PeFTs.

Openrouter fking sucks and I don't know why people here act like it's so great. Stop using it if you care about local AI and accept that the cost you'll pay for tokens is higher than you will when consumed via any cloud. That's the price for privacy, control, and better quality via inference time optimizations that otherwise aren't available.

Agreed. To put this in perspective, batch 1 token decode is bandwidth limited in theory.

Memory bandwidth of RTX 3090 is listed as 936GB/s. The post isn't fully clear on which model they used and how big it is, but even assuming it perfectly filled the 24GB of that GPU, 30tok/s means the achieved bandwidth is only 720GB/s. There's a bunch of room for improvement here even without MTP, and those improvements should largely stack with MTP.

I've been using https://spark-arena.com/leaderboard to glean this kind of information for DGX Spark, a sort of recipe book. The Nvidia forum has people talking about the things you wish to know. I see some on Discord/Reddit/et al, but less cohesive

I've switched from using the spark as a way to run one model as best it can to running several support models for the md kb I'm working on

2xRTX5080 would be awesome. You'd only be able to run a q6, which it's already pretty good, but moreover you'd be able to use P2P and use Blackwell full speed, which I can't.

I am actually surprised with the power draw, the box itself idles at 20W, which already amazes me for a Ryzen; when computing, I barely pass the 600W bar, and as I am not really using it to vibecode an entire system, I don't even notice the spikes on the power monitor (Shelly + homeassistant).

This one: https://es.aliexpress.com/item/1005010123289822.html?spm=a2g...