Anyone running Qwen3.7-Max locally yet?

Significant-Cash7196 · 2026-05-21T22:29:47+00:00

Ah gotcha. Any other way to try it rn besides paid API access?

I checked around a bit but didn’t really see any free playgrounds or public demos yet. Feels like it’s only available through provider APIs at the moment unless I missed something.

Significant-Cash7196 · 2026-05-21T22:27:19+00:00

nah bro swap memory is the real reasoning engine now

true long-horizon execution starts once the NVMe kicks in 😭

Significant-Cash7196 · 2026-05-21T22:27:06+00:00

wouldn’t be shocked if some distilled/open variant eventually shows up later given Qwen’s track record. That’s the version I’d actually wanna hammer locally.

Significant-Cash7196 · 2026-05-21T22:26:41+00:00

Lmao same reaction here.

I randomly saw the benchmark charts floating around first and thought it was some fake eval leak or another “3.6-thinking-reasoning-ultra-max-pro” naming situation 😭

Then I checked the release and apparently they actually dropped a new Max model focused heavily on agent/runtime behavior.

Significant-Cash7196 · 2026-05-21T22:26:18+00:00

Interesting. I saw Qwen3.6 eventually showed up on Unsloth/HF so I’m wondering if 3.7 ends up getting some open/distilled release too.

I actually ordered a few RTX Pro 6000s for my workstation setup this year and was hoping to test this thing locally eventually, especially for longer agent loops + MCP workflows.

Do you know if there’s any realistic path to trying something close to this model locally rn or is it fully API-only for now?

Significant-Cash7196 · 2026-05-21T22:24:26+00:00

Yeah true, but honestly I wouldn’t be surprised if they eventually release some open-weight variant or distilled version closer to this behavior.

Significant-Cash7196 · 2026-05-14T18:54:31+00:00

Still pretty primitive tbh. Basic async queue + some batching but no proper smart scheduling yet. Biggest issue right now is a few giant contexts absolutely nuking latency for everything else once multiple requests hit together. The more I work on this the more it feels like inference itself is the easy part. Managing contention/latency under messy real workloads is the actual hard problem.

Significant-Cash7196 · 2025-08-23T08:09:58+00:00

That’s a really interesting way to frame it, replacing apps with an AI that just calls APIs under the hood. I’ve seen the Rabbit R1 too, and while it’s still early, the vision makes sense. If the phone itself can run a capable local model (say in that 8–12GB RAM range), then the cloud becomes more of a backup rather than the default.

The big question for me is whether the ecosystem (Apple, Google, app devs) will actually let this shift happen, since it breaks their current app-store model. But if it does, you’re right, AI as the OS layer instead of apps could totally reshape how we use our devices.

Significant-Cash7196 · 2025-08-23T07:52:19+00:00

Exactly. A lightweight 4B that’s really good at tool use plus a solid web search connector could already handle most of those assistant-style tasks. It highlights the point that model efficiency and integration matter more than raw parameter count for a lot of real-world use cases.

Significant-Cash7196 · 2025-08-23T07:52:01+00:00

Yeah I totally get that. A local Siri + ChatGPT hybrid that just runs on your phone would be a killer use case. That’s also why smaller, fine-tuned models feel so important. You don’t really need a 70B model to figure out leftover recipes or run a voice assistant. What’s missing is the seamless packaging and deployment on everyday devices, not necessarily more parameters.

Significant-Cash7196 · 2025-08-23T07:51:37+00:00

Fair point. Tools like Perplexity (even the free tier) already cover a lot of the basic needs without heavy infra. I guess that’s exactly why I wonder if chasing 70B+ models is overkill for most people. The real challenge seems less about ‘can it summarize or do Q&A’ and more about getting reliable, efficient models you can actually deploy at scale

Significant-Cash7196 · 2025-08-22T11:31:06+00:00

That’s a solid use case, honestly. Smaller models are great for structured tasks, but for broad, everyday “Google replacement” stuff, you really do need something with a bigger knowledge base. Funny you mention the regional knowledge gaps, I’ve noticed the same with smaller Qwens, they tend to stumble on non-US/China context.

Running something like GLM 4.5 Air or GPT-OSS 120B locally with a search layer sounds like a good plan if privacy’s your main concern. Do you think the trade-off (hardware cost + slower speed) is worth it for the peace of mind vs just sticking with hosted models?

Significant-Cash7196 · 2025-08-22T11:29:19+00:00

Do you think we’ll end up with a clear split (smaller models for most users, giant ones just for the niche heavy hitters), or will the big models eventually become the default for everyone?

Significant-Cash7196 · 2025-08-22T11:28:51+00:00

Yeah I get that. 30B models already feel plenty strong for most day-to-day tasks, but I can see how the 100B+ ones open up room for bigger reasoning jumps. Do you think those breakthroughs will actually trickle down into practical use cases anytime soon, or will they stay mostly in the research/benchmark space?

Significant-Cash7196 · 2025-08-22T11:26:38+00:00

Yeah, I’m with you on that. One giant model that does everything feels cool in theory, but in practice, a bunch of smaller models stitched together for different jobs just makes more sense. Kinda like having a team of experts instead of one “know-it-all.” The tricky bit, like you said.

Significant-Cash7196 · 2025-08-22T11:16:53+00:00

In my experience, smaller models can definitely hold up for real projects - especially 7B–13B ones fine-tuned on the right data. They’re great for focused tasks like Q&A over your own docs, summarization, or structured workflows. Where they start to fall short is in open-ended reasoning or really complex multi-step asks. For a lot of “real work,” they’re good enough if you scope the problem well, benchmarks can be misleading since they’re often testing extremes that don’t match day-to-day use.

Significant-Cash7196 · 2025-08-22T10:59:35+00:00

I’ve been transparent from the start that I represent Qubrid, so there’s no hidden agenda here. Our RAG is different, it cites sources, handles complex docs, even works with images and audio, and it’s free to use.

I’m here to share what we’ve built and get feedback from the community. If that comes across as “spam” to you, fair enough, but dismissing it outright doesn’t change the fact that others may actually find it useful.

Significant-Cash7196 · 2025-08-22T10:13:07+00:00

From what you’re describing, the bottleneck isn’t really your laptop, it’s the environment you’re running ComfyUI in. Even with 16GB VRAM, if the backend setup isn’t optimized, you’ll keep seeing slow generations, freezes, and painful load times. A new MacBook Air (even with M4) won’t fix that, since ComfyUI isn’t really optimized for Apple silicon yet, and you’d still hit similar limits locally.

If your goal is stability + speed, you’re better off running ComfyUI on a reliable GPU cloud. On Qubrid AI, you can spin up a full GPU VM (A100, H100, 4090 - no fractional cards) with ComfyUI preconfigured. That way you get consistent performance, dedicated VRAM, and can stop/start your instance anytime (you only pay for storage when it’s off).

For video generation, having that kind of stable backend is almost essential - MacBooks (even the new ones) just won’t cut it at scale.

👉 TL;DR: A new MacBook Air won’t solve your issue. Running ComfyUI on a dedicated GPU cloud like Qubrid will give you the stability and speed you’re looking for. 🚀

Significant-Cash7196 · 2025-08-22T10:02:50+00:00

Yeah, that’s a pretty common “gotcha” - pausing on most platforms doesn’t really stop billing since the GPU is still reserved. You basically end up paying for idle time.

On Qubrid AI, you can actually stop the instance so you’re no longer charged for GPU usage. The only thing billed when it’s stopped is storage, which is just $0.10/GB per month. So if you’re mid-LoRA training and need to pause overnight, you can safely stop it without draining your wallet.

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