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Qwen3.6-35B is my daily driver for AI, and what convinced me to cancel my Claude subscription back in April. The Qwen3.6 line is easily the best local model I'v
by nozzlegear 3mo ago
Qwen3.6-35B is my daily driver for AI, and what convinced me to cancel my Claude subscription back in April. The Qwen3.6 line is easily the best local model I've tried, and I've tried a lot. I've got it diligently grinding away on my laptop right now, reviewing and fixing some bugs in my F# code.
- pettijohn 3mo ago35B MoE is certainly a good and fast local model. I find 27B dense to be quite a bit smarter, so I daily drive that. I wish there was a ~100B MoE with maybe 10B active. It would be super smart and fast!
- nozzlegear 3mo agoI've heard 27B is smarter! I tried it some time ago but couldn't get it working with my oMLX. I need to try it again.
- mattnewton 3mo agoHonestly the 27b dense one punches way above its weight in a lot of domains, especially coding in my testing, so I think you will probably be disappointed.
- npodbielski 3mo agoIn my case I would say they are comparable but moe models are looping and getting lost a lot more than dense models. On the other hand having 90t/s with any local model is nice and Pi with loop police extension can prevent looping a lot.
- cyanydeez 3mo agoLooping seems related to quantization and not the model itself. If youre digging deep into quants to get working context then yeah.
- mattnewton 3mo agoThere was a 3.5 122B 10A release - https://huggingface.co/Qwen/Qwen3.5-122B-A10B https://huggingface.co/Qwen/Qwen3.5-122B-A10B
- kanemcgrath 3mo agoI tried it for a bit, and It was not really worth its size. It got swept up in all the other AI news recently, but laguna s 2.1 I think is the best ~100B moe model right now
- nozzlegear 3mo agoI didn't mention it above, but Laguna S is my other favorite model. I use Qwen a lot more, it's smaller and faster, but I like to switch to Laguna when I feel like I need a "heavy hitter" for certain huge or complex tasks.
- tommica 3mo agoWhat on earth hardwares do you guys have to be able to run 100gb models locally?! That's crazy! I'm here struggling to even get 27b models to run in somewhat usable way
- nozzlegear 3mo agoHaha I'm on an Mac Studio with an M1 Ultra, 64gb ram. I bought it when it first came out, it just happens to be good for local LLMs. I have to use a smaller quant of Laguna S though (I think 4-bit? Not at my machine to check), as 8-bit and full size definitely don't fit in the 64gb I have.
- lcnPylGDnU4H9OF 3mo agoYeah, a good rule of thumb is that the weights take up ~100% of the size of the model, so 100B bytes (8-bit quant) would be, well, 100GB and a 4-bit quant would be half that.
- antman 3mo agoA 35 A3B as smart as previous gen 27B would be a sweet point
- wickedsight 3mo agoI use 27B in plan mode and 35B MoE in act mode. I noticed that is the best balance for me for consistent tool calls and intelligent planning. Takes some time to switch, but it's worth it for me.
- julianlam 3mo agoWonder if it's possible to share a common cache via cachyllama between 27B and 35B-A3B
- traceroute66 3mo ago> 35B MoE is certainly a good and fast local model. I find 27B dense to be quite a bit smarter Isn't that just the definition of MoE vs dense ?
- cyanydeez 3mo agoFull name is 35B-A3B. 3B Is the token generator thats selected out of the 35b available in the model, which is some layered jazz. So it can be dumber but its quite capablr.
- neumann 3mo agocompared to claude - how 'fast' is it in terms of throughput on your laptop?
- syntaxing 3mo agoI use it with a strix halo server. 35B runs stupidly fast. 27B is about 700 TPS prefill and 30 TPS token generation. Which interestedly is about what Kimi K3 gives me depending on provider.
- dionian 3mo agowhat hardware do you use or recommend for this? never heard of it until today.
- Zetaphor 3mo agoStrix Halo is the unified memory platform from AMD. Similar to the DGX Spark from NVIDIA or the M series Macs. I personally have the Framework Desktop, but there's also systems from other brands like Bosgame
- vrganj 3mo agoYou can also get it in a laptop form factor that feels like a MBP with a nicer keyboard if you get an HP Zbook G1A! Huge fan of that thing, it's th e Linux MBP I've always wanted.
- Zetaphor 3mo agoWhile the laptop option is nice, for an inference server you're probably going to want the desktop form factor as it has significantly more thermal overhead and thus better performance. In the desktop models most of the internal volume is a gigantic heatsink
- leansensei 3mo agoRTX 4060 and above. Ideally RTX 50 Series, because you can run NVFP4-quantized GGUFs that give you better prefill AND better quality.
- ufish235 3mo agoWhat laptop?
- nozzlegear 3mo agoIt's just a MacBook Air with an M4, cheap and nothing special. I host Qwen on my Mac Studio, an M1 with 64gb ram. The model uses around 20-25gb ram depending on what it's doing.
- brailsafe 3mo agoI wonder if I could get this running on my 48gb M4 Pro. Haven't been able to load anything beyond 27B
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- razster 3mo agoI would recommend looking into Ornith1.0 - it's using Qwen3.6 35B-A3B and excels in coding, at least for my coding needs, Python, web-dev, SQL scripting and some C#. Using Pi harness.
- mraza007 3mo agoI have been using Qwen3.6-35B-A3B as my daily driver as well and its been phenomenal when it comes to coding
- coverband 3mo agoHow do you use a 72GB model as your daily driver locally?
- chmod775 3mo agoAt 4 bit it easily fits in 32GB. That's what most people use.
- sznio 3mo agouse a quantized version. since it's MoE, what matters is that the 3b parameters that are used for every token fit in gpu vram, the rest can stay in system ram. really great if you don't have unified memory.
- anon373839 3mo agoApple Silicon. But: there's no need to use the FP16 version. At 8-bit precision the quality loss is almost imperceptible. That cuts the footprint to 36GB. Which is great for a 64GB Mac, because you have room for plenty of context. 6-bit also works nicely at 26GB + context. You want to use the newer quantization formats like Unsloth's UD quants or oQe, where the weights are selectively quantized using a calibration dataset so that important weights are left at/closer to full precision.
- trollbridge 3mo agoQwen-3.6-35B-A3B was our "gateway drug" into switching our organisation to agent/harness-first coding. Particularly, I had one team member who was extremely sceptical of AIs/LLMs/harnesses and refused to use them. One day he said "Well, I have an RTX 5090 doing nothing... should I try to get something up on it?" and a few minutes later he had 3.6-35B loaded up, running OpenCode. It continues to be a workhorse to this day, running on both my local Mac for various types of jobs, an AMD R9700 at the office, and said teammember still uses it on his 5090, although in practical terms we do a lot more with DS-V4-Flash-0731 these days.
- websap 3mo ago[flagged]
- badsectoracula 3mo agoA local model needs 0 investment and 0 commitment, takes literal minutes to get started (especially if you have someone who is into that stuff showing you the ropes) and if you end up disliking the experience of using AI you can just `rm -fr` it and forget the whole thing existed.
- jiggawatts 3mo agoThis is the diametric opposite of the rent-vs-buy scenario that this entails. Local: You need to invest $thousands into GPU and/or very-high-end CPU+Memory hardware. Vendor: You can use any existing device, even a phone or tablet. A very low-end laptop is fine. > takes literal minutes to get started Local: Typical scenario is hours just to download the software, the model weights, and then faffing around with CUDA and matching your GPU drivers. Vendor: Free-tier available instantly on a web URL. Even local agents have free tiers from multiple vendors. Install is a single command and/or download and "next,next,next,finish" wizard that takes ~1 minute. > you can just `rm -fr` it and forget the whole thing existed. I'm still cleaning up multi-GB model weights floating around in hidden subdirectories under my user profile from months ago when I was experimenting with local models! Meanwhile I simply... stopped using Gemini. That was the entire process: I no longer actively use it. They stopped billing me for my token usage, because it is now zero. That's... it. You have it totally backwards.
- bckr 3mo agoWhat are the specs of your laptop and what tokens per second do you get?
- nozzlegear 3mo agoIt's just a Macbook Air with the base M4 and 16gb ram, but I'm hosting the models on a Mac Studio with M1 Ultra and 64gb ram that I had purchased when it came out. I get about 45-55 tokens per second with this setup. I think I could get more if I spent some time fiddling with the parameters, but I don't really know what I'm doing there so I've just left most of it on oMLX's defaults.
- apexalpha 3mo agoIt was for me too but the new deepseek pricing is too good to ignore for now. I honestly think that with my electricity prices running qwen 36B myself is more expensive than hitting the cache rate at deepseek.
- sparkling 3mo agoCan you elaborate on DeepSeek (deepseek-v4-flash, i assume?). What does your typical usage pattern look like and what is your weekly/monthly spend? I gave it a try for a few days (pi + openrouter + deepseek-v4-flash via deepinfra) and ended up paying ~$18 for rather light usage. Yes it's still cheap, yes it's fast, but i feel i would still get a better deal with a Claude subscription plan.
- maattdd 3mo agoDeepSeek without OpenRouter is wayyyy cheaper
- sparkling 3mo agoIs it? OpenRouter shows DeepInfra being cheaper than DeepSeek directly https://openrouter.ai/deepseek/deepseek-v4-flash-20260731#providers https://openrouter.ai/deepseek/deepseek-v4-flash-20260731#pr...
- maattdd 3mo agoInteresting. My anecdotal experience is that it is.
- thecopy 3mo agoI agree with parent. OpenRouter might be cheaper list-price, but i have been using 10$ on DS platform since April/May, still have 2$ left. Using OpenRouter i depleted the same dollar-amount in a 1-2 weeks with same usage pattern. No idea why.
- 3mo ago
- westpfelia 3mo agoI'm a big ole noob when it comes to local AI. What are you using for a harness? Or platform to interact with it?
- ch_sm 3mo agoyou can try ollama, omlx or llama.cpp for instance to download a model and get an inference server running locally. They expose „open ai compatible“ endpoints, so you can configure almost any harness to use them.
- jlkuester7 3mo agoI recommend trying pi.dev as your agent harness for local models. In my experience it has been the sweet spot of functionality (which you can and should extend with plugins) vs performance (OpenCode just swamps local models on my hardware).
- kumarvvr 3mo agoOn what hardware do you run the model locally, if so?
- anon373839 3mo agoNot the GP, but I run this model as daily driver too. It runs great on a Macbook Pro 64GB (M3 Max). Token generation speed can be about 100 tokens/sec with multi-token prediction, although it depends on the context. Worst case speed is around 50 tokens/sec. The weaker point is prompt prefill, which starts at 1,400 tokens/sec but decreases significantly at high contexts. That said, for agentic scenarios, if you're using a harness that doesn't needlessly bust the cache, it doesn't feel slow. I really hope they release a Qwen 3.8 35B, although the lack of a mention seems ominous.
- cicko 3mo agoWith laptop being ...?
- nozzlegear 3mo agoIt's just an M4 MacBook Air with 16gb ram – probably incapable of running models itself. I actually run the models on my Mac Studio which is an M1 Ultra with 64gb, and oh-my-pi on my laptop is configured to use the models over the local network.
- wookmaster 3mo agoYeah I have 24 GB Ram on my M4 and am disappointed in the capabilities. You really need a ton of RAM. I can run some basic 8B models fine but they dont do well at all in basic coding stuff I've thrown at them.
- quanto 3mo agoIs your Qwen3.6 locally run on your laptop? What kind of tokens/s are you getting from your laptop GPU?
- saagarjha 3mo agoI have Qwen3.6 35B-A3B on my laptop and it does 60 tokens/s
- nozzlegear 3mo agoQwen is running on my Mac Studio, an M1 Ultra 64gb. My harness (oh-my-pi) on my laptop is configured to use the models hosted on my local network, since it's just a MacBook Air 16gb and probably incapable of running anything useful itself. I get about 45-55 tokens per second using Qwen with this setup. I could probably squeeze out more if I messed around with the settings, but I'm mostly using oMLX's defaults for the model.
- btbuildem 3mo agoI'm on the verge over here, the new Anthropic models have been a disappointment. I've tried the A3B variant, but had mixed results. What do you use as the coding agent, and have you heavily customized your workflows?
- ncphillips 3mo agoWhat kind of machine do you have running that? My attempts at local have always resulted in a very hot lap
- AbsurdCensor 3mo agoStrix Halo for me. If I am running something on my laptop, it's a much smaller usually around 12b model, but those are a bit less functional. I mean I think there is a a ROG FLow Z that has the Strix Halo setup, but that thing was super expensive.
- nozzlegear 3mo agoI host the models on my Mac Studio, an M1 Ultra with 64gb ram (I bought it when it came out, just happens to be good at LLMs). So when I work on my laptop, I have my oh-my-pi setup configured to use the models on my Mac over my local "bonjour" network or whatever Apple calls it. That way I have a nice cool lap, while using models that my M4 MacBook Air with its 16gb ram couldn't possibly run.
- ncphillips 3mo agoCool yeah. I got a M3 with 32GB ram and it’s a little iffy. I’ve considered getting a MacMini to act as an in-house
- bibstha 3mo agoWhat do you use to pair it with web search?
- AbsurdCensor 3mo agoDepends on how you are doing it. LM Studio and tool calling models can use the web, or you could go for something like Perplexica, or if you want to go real crazy, something like Hermes or OpenClaw.
- nickthegreek 3mo agoI use searxng.
- bmitc 3mo agoHow do you run Qwen? I tried setting it up the other day and got really confused with all the options between Ollama, LM Studio, and llama.cpp and also the bazillion different Qwen models available.
- alasr 3mo ago- See "Llama.cpp vs Ollama"[0] and "Llama.cpp vs LM Studio"[1] for a high-level features comparison and find out which one suite you best (check out the "Target Users" category[0][1], at least). - If you're still not sure, "LM Studio"[2] is ok to start with as you'll be able to download, start/stop/manage and chat with your LLM model all in one place: a single desktop app. Also, once installed, enable "Developer mode" under "Settings > Developer" tab; you might find it useful later. - Regarding LLM models you can, based on your hardware spec, start with Qwen3.6, Google's Gemma4, OpenAI's gpt-oss-20b or Nvidia's Nemotron; use LM Studio's "Model Browser" screen to search for LLM models (each one listed with their organizational name/brand & logo: ignore the one you don't recognize as you might not need them at the start of your journey; you can always revisit them later, if needed.) - Regarding which quantized LLM models you should download & run, just go with the default "LM Studio" selection (at least, in the beginning). Later, you can experiment with other quantization values to find out which one works best for your use cases. I hope it helps. --- [0]. https://llama-cpp.com/llama-cpp-vs-ollama/ https://llama-cpp.com/llama-cpp-vs-ollama/ [1]. https://llama-cpp.com/llama-cpp-vs-lm-studio/ https://llama-cpp.com/llama-cpp-vs-lm-studio/ [2]. Ignore "LM Studio Bionic" for now; just download and install "LM Studio" from https://lmstudio.ai/download https://lmstudio.ai/download
- bmitc 2mo agoThanks! Unfortunately, LM Studio does not support network proxies, and Ollama does not support Qwen3. So it seems like Llama.cpp is the only solution.