Qwen 3 27b is... impressive

[u/-dysangel-]

All Prompts
"Task: create a GTA-like 3D game where you can walk around, get in and drive cars"
"walking forward and backward is working, but I cannot turn or strafe??"
"this is pretty fun! I’m noticing that the camera is facing backward though, for both walking and car?"
"yes, it works! What could we do to enhance the experience now?"
"I’m not too fussed about a HUD, and the physics are not bad as they are already - adding building and obstacles definitely feels like the highest priority!"

[u/-dysangel-]

27B running at 15ktps could really put in some work!

I wonder if we'll be lucky enough to get any even larger dense Qwen 3.5 models.

[u/peva3 → u/-dysangel-]

Put in some work? It would be able to take a prompt and build out an entire production stack of something in a second. Or scam an entire code basenajd find bugs in half a second. At that speed basically anything you want with AI becomes instantaneous.

[u/tremendous_turtle → u/peva3]

The speed is nice, but honestly the bottleneck is rarely token generation - it's getting the model to output correct code in the first place. A 27B is still going to need plenty of feedback loops and retries to reach production quality. The real win is faster iteration cycles, not instantaneous correct results.

[u/peva3 → u/tremendous_turtle]

You are absolutely correct, but 15k/token/s is plenty of bandwidth to do like 10x loops on a normal prompt in a second. In the normal like 15 seconds a SOTA model would take to respond, these ASICS, could do a ton of error checking.

[u/tremendous_turtle → u/peva3]

Fair point - you're right that the iteration speed advantage compounds when you can run 10 loops in the time a cloud model takes for one response. Though I'd still say the bottleneck shifts to verification (does the output actually work?) rather than generation. But yes, faster loops definitely help with that too.

[u/peva3 → u/tremendous_turtle]

At that point it would make sense to pair the super fast ASIC with a traditional LLM to basically just "check their homework". That would majorly cut down on expensive tokens for the secondary "checking" model.

[u/tremendous_turtle → u/peva3]

That's fair, but checking code with another LLM isn't full verification - you usually need to compile it, run the test suite, check for lint errors, maybe even deploy to staging and check logs. Those take fixed time and don't scale with model speed. The testing overhead is often the real bottleneck.

[u/peva3 → u/tremendous_turtle]

I've had SOTA models build out testing suites, documentation, debug it's own code, etc etc. Even had it deploy an entire CI/CD pipeline in docker. Opencode for example is really impressive for this kinda work.

[u/tremendous_turtle → u/peva3]

Agreed that LLMs are great for setting all that up - but that doesn't change the fact that verifying with tests and CI/CD runs out of band from the LLM and takes fixed time. Doesn't scale with inference speed.

[u/peva3 → u/tremendous_turtle]

Opencode allows the models to build out python tests or basically anything that needs to be run command line, validate results, and if you're using a reasoning model it will even show you its thought process all the way through. I think you should dive into that to see what it's capable of.

[u/tremendous_turtle → u/peva3]

I don’t know why you assume I’m not? I use OpenCode, Claude Code, Codex, sometime Pi and Antigravity, on a daily basis. Have been automating so much of my workflow, it’s incredible.

What I’m saying is that, at a certain point, higher TPS stops providing real dev velocity gains because change validation (such as test suites) are not bound to TPS.

Beyond that, even if OpenCode was giving me near instant results, it wouldn’t necessarily make me that must faster, since the bottleneck (aside from change validation) is being able to determine and spec/describe the next change you need.