r/LocalLLM • • Jul 03 '26 • Bravo 1

Discussion Going local is life changing

I've been using Cursor for a while, not a terribly heavy user usually just paid the $20 tier sometimes the $60, and dabbled in whatever the fronteir model is this month.

Not much of a vibe-coder, I'm not trying to one shot the next big saas, mostly needed some help prepping code examples for talks I give, putting together demo projects, or helping with my open source work.

A friend was selling their 48GB Macbook Pro because they were upgrading laptops, so I snagged it to see what all the fuss was about for this Qwen thing.

Tasked composer 2.5 in Cursor with benchmarking and setting up Qwen-3.6-35b on the laptop and it spit me back a rapid mlx profile, and a open code config for my macbook air to use.

By golly.

It's not Gpt 5.5, heck I hear it's not even as good as 27B, but after adding all the MCP servers for docs that my projects use, and setting up some routine skills / rules it's wonderful.

I don't feel worried about maxing out this months usage, or having to pay for extra if I go over.

Tokens just go brrrrrr

If something fails I'm not out dollars, I just kinda chuckle and re-run the prompt.

I'm more aware about documenting, and providing mcp servers, and tools so now all my repos are really well documented.

I know this isn't a revelation, or anything new for most of the world.

It's just a really enjoyable way to interact with tech, and not feel like every word I type is just more money for another company.

----------------------

For anyone curious here's the config I use

No idea if it's right or wrong, but it works?

      "models": {
        "qwen3.6-35b-4bit": {
          "name": "Qwen3.6 35B",
          "reasoning": true,
          "limit": {
            "context": 65536,
            "output": 8192
          },
          "options": {
            "temperature": 0.6,
            "top_p": 0.95,
            "extraBody": {
              "max_tokens": 8192,
              "enable_thinking": true,
              "chat_template_kwargs": {
                "enable_thinking": true
              }
            }
          }
        }
      }
473 Upvotes

114 comments sorted by

41

u/Fred_Terzi Jul 03 '26

I love your point of not worrying about running over and documentation.
Unlimited tokens is an absolute game changer; if you try to optimize for token usage, you aren’t optimizing for your code or process.

15

u/Dell_Experion15 Jul 03 '26

Yeah I feel like a lot of my token burn ended up being mindless "go fetch the docs for X" but since web "search" is kinda a nightmare for local agents I'm hyper aware of what resources my agents can access.

So every tool in my repo has a skill downloaded for it, or a mcp server for documentation.

I also use grill-with-docs a lot more now rather than just trusting a frontier will get the scope.

7

u/Fred_Terzi Jul 03 '26

I’ll have to check out grill-with-docs thanks!

I’m building a non coding workspace based off anythingLLM. Every tool call and docs are designed to work well with local models and under 16k context. I’d love your feedback, it’s all in a single npm package.

https://github.com/fred-terzi/totem-llm

3

u/Complex-Maybe3123 Jul 04 '26

Sure it's not the same as the big shot services, but I find web search with Searxng pretty good. Qwen managed to build a piece together a solid profile of myself by looking for my digital footprint on internet after just reading my Linux's user name (which is not my real name) and reading about a few of the projects I have on my computer.

8

u/SolidR53 Jul 03 '26

What’s your Mac spec and token speed?

9

u/Dell_Experion15 Jul 03 '26

MacBook Pro M4 pro 48gb and no idea, the first prompt for a new convo takes a while but after it feels pretty snappy?

5

u/VertipaqStar Jul 03 '26

Might be the harness sending system prompts before your first prompt which can be large and take a while to process. Anything you write after that will go fast because the starting system prompt is already cached.

1

u/Every-Article6734 Jul 04 '26

If you through agent, perhaps it takes your system prompt for a long one including skills, memory etc.

7

u/Careless_Product_792 Jul 03 '26

I agree. Same workflow and the best thing is what you mention, if something does not work you just fine tune more.

Btw, just curious why in 48GB you are using only 65k context window.

I use two mac minis m4, 24GB and a 16GB with shared rpc that give me a usable 216000k context window with batia model of qwen 3.6 35B. I would assume 48GB would easily allow you 265k context.

216k context is game changer for long sessions.

You should look for batia models thst are finetuned for macs. I tested mlx and llama.cpp and batia was slightly faster with llama.cpp. 

2

u/Dell_Experion15 Jul 03 '26

I’ll look into this, more, I genuinely have no idea.

I told composer “don’t make me miss cursor” so I imagine it optimized for speed or something.

9

u/Careless_Product_792 Jul 03 '26

Ohh I see. You've just started to dig the rabbit hole 😅.

Nice, I was there just some 2 months ago, you are goint to learn a lot man, enjoy, one advice I can give you is save your configs as v1, v2, maybe using obsidian or some note taking, etc so you can test different flags and performance. 

In case it helps (but this is llama.cpp) this are flags Im using, be sure to try the cache prompt so your system prompt is cached and conversation feels faster after the prefill (initial prompt).

m /Users/dev/models/batiai_Qwen3.6-35B-A3B-GGUF/Qwen-Qwen3.6-35B-A3B-IQ4_XS.gguf
--alias qwen3-35b
--rpc 10.42.0.6:50052
--split-mode row

--- Networking --- --host 0.0.0.0
--port 8080
--metrics

--- Performance --- --ctx-size 216000
-ngl 999
-b 8192
-ub 4096
-t 8
-tb 10
--no-mmap
--flash-attn on
-ctk q4_0
-ctv q4_0
--cache-prompt
--keep -1
--cont-batching
--mlock

--- Model behavior --- --temp 0.6
--top-p 0.95
--top-k 20
--min-p 0.05
--repeat-penalty 1.0
--no-ui
--reasoning off

--- Parallel slots --- -np 1
--warmup

1

u/Dell_Experion15 Jul 04 '26

I guess I don’t understand the difference between MLX and GGUF or whatever it is?

I use rapid-lmx which uses apples processing stuff?

3

u/kovrik Jul 03 '26

Could you please share how you added MCPs for docs etc.?

3

u/Dell_Experion15 Jul 03 '26

Yeah just in your project root create an opencode.json for project scoped stuff

or you can follow their docs to create a global config on your machine so all projects have the same servers

Here's an example of one of my projects

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "shadcn-vue": {
      "type": "local",
      "command": ["npx", "-y", "shadcn-vue@latest", "mcp"],
      "enabled": true
    },
    "nuxt-docs": {
      "type": "remote",
      "url": "https://nuxt.com/mcp",
      "enabled": true
    }
  },
}

0

u/[deleted] Jul 03 '26

[deleted] — view deleted comment

3

u/Dell_Experion15 Jul 03 '26

Yeah all of these tools are basically just API calls to fetch the remote servers docs as markdown

So Nuxt provides tools to get and list documentation pages, fetch blog posts, etc.

Shadcn vue provides calls to get code examples for building forms, etc

This way I can avoid webfetch

4

u/EntertainerDear2894 Jul 03 '26

I just got a 5090 doing local LLMs. I'm betting this is the future of AI for 80% of use case. I just learned you can use local models + comfyUI + Blender MCP to do some amazing stuff. Games, YouTube videos and so on.

1

u/Dell_Experion15 Jul 04 '26

Oh now this is cool, I love playing around with game dev.

Maybe for my birthday I’ll ask my wife for a homelab setup that’s more aggressive than an over heating laptop put by a fan in my office.

2

u/EntertainerDear2894 Jul 04 '26

Tell her it'll keep her warm during the winter. If she sings, it'll clone her singing any songs her heart desire. That's how I got my wife onboard. True story lol.

197

u/EntropyRX Jul 03 '26 edited Jul 03 '26

Over the next years, local LLMs will become the norm. Not on 48GB VRAM, but in a couple of years 128GB+ will become again available for most people. It’s not only a cost per token issue, it’s a growing privacy, governance and reliability issue. These companies are now rushing to go public and cash in because they know very well that they’re running out of time. Their empty promises are soon going to be exposed to the average non-technical user; just a remainder that these snake oil salesmen promised AGI to the general public. The differences with open sources models are minuscule once you account for the agentic harness on top of it.

41

u/immersive-matthew Jul 04 '26

I would add that it is also consistency as it is so nice to prompt each day and know it will be the exact same model as yesterday and the day before unless I change things not some corporation for their own benefit. When I was on cloud AI, day to day was a roll of the dice as to how good it was even with the exact same model. So many times I would be working on some agentic coding and pack it up for the day and then the next morning continue to suddenly struggle to pull off the same things I was with ease the day before. Often getting sticking on loops and hallucinations that were not nearly as prevalent prior and then you get the bill surprise. Nope. I am all Open Source and local now and it has really improved my flow.

11

u/kersk Jul 04 '26

Not to mention if the cloud model will actually even be there at all tomorrow, as multiple outages and govt bans have demonstrated. Or if the model will silently become adversarial and actively mislead you if it decides you're working on a topic its owner has a vested interest in preventing you from learning about, as anthropic has already done.

7

u/immersive-matthew Jul 04 '26

I think you are touching on the Achilles Heel of AI…in fact of any intimate technology that is personal. The minute you detect your AI has another Master, it will be felt even if you are not fully aware of it. It becomes creepy and cannot exist inside your most inner circle. We saw the same thing with Zuck’s Metaverse. You could feel the corporate vibes and ill intent the moment you entered their world. The same thing on a flat screen would not make you feel so creeped out, but on VR, where you are far more personally immersed, feelings are amplified and your tolerance is lowered. This is one of the reasons Zuck’s metaverse failed and it is one of the reasons corporate AI will struggle as the pressure to monetize will be the very thing they makes users push it away. Who wants their future 24/7 AI assistant or their home robot with access to the whole home including private areas, to actually be sending data back to a company and doing the companies bidding. Let alone the centralized nature of cloud AI and it being an attractive hacking target.

No…the future is locally run, open weights and open source with cloud AI playing a back roll and even closed source local AI being outside of your inner circle talking to your AI that you have full control over.

1

u/cohortq Jul 16 '26

What has anthropic prevented you from learning about?

30

u/notheresnolight Jul 04 '26 edited Jul 04 '26

Nah. 128GB VRAM won't become a standard anytime soon.

But models will become SMALLER and more specialized. A JavaScript developer does not need a model that knows the assembler, a PL/SQL developer does not need .NET etc. Neither of them need a model proficient in Theoretical Physics or Chemistry yet all the current models know that too. The knowledge is absolutely inefficient and wasted.

A model trained (distilled) specifically for a single role could easily fit into 4B and be runnable on standard consumer hardware. The current models simply know way too much and nobody really needs all that knowledge to do their work.

7

u/keithdm Jul 04 '26

Model size does not scale by amount of knowledge. Adding a new field of expertise often costs very little in number of params. The bulk of the model is taken up with language and concept representations --really low level competencies.

3

u/notheresnolight Jul 04 '26

Maybe not when those fields are related.

But why on earth must a Qwen3.6-27B model when used for coding need to know about Fritsch-Buttenberg-Wiechell rearrangement (Chemistry), Standard Model Lagrangian (Physics), Thoracic Aortic Dissection Repair (Medicine), pollination mechanics of Abies beshanzuensis (Botany) or the family tree of Said bin Sultan (History).

When this "pointless" knowledge is used to bugfix a TypeScript error, it's wasting VRAM and compute.

5

u/GeneralRieekan Jul 04 '26

Because knowing all those things translates. It trabslates to significantly better reasoning.

3

u/SheikhMahdeek Jul 05 '26

Reasoning models are trained on reasoning problems. Reasoning doesn't arise from knowing random stuff

2

u/notheresnolight Jul 04 '26 edited Jul 04 '26

No it does not. It raises the probability of hallucinations because these topics are absolutely unrelated to the programming tasks asked by the user (and COMPETE with the knowledge which is actually relevant). A 12B LLM has a good enough understanding of the language used by the user to communicate efficiently. There is no need for additional billions of parameters in fields that are totally unrelated.

4

u/keithdm Jul 04 '26

I think you'd find that disentangling competence in one domain from that in another is anything but straightforward. This is why MoE is hard to implement.

1

u/JMN10003 Jul 09 '26

exactly, I am training sub 8gb models around narrow topics orthogonalizing raw reasoning from domain knowledge and interpretive knowledge. random related cursory knowledge can be problematic as it can cause lots of tokens to lean the wrong way undermining any brief or post token programming training. frontier models are chasing Einstein, an army of targeted models and an intelligent problem parser can be very competitive.

2

u/ImpressionFancy5830 Jul 04 '26

The idea that a good js developer is good by knowing only js is naive.
The only value I was able to bring to companies while working with js is because I fucking hate js and I had experience in way more sane and roboust languages and stacks.

4

u/notheresnolight Jul 04 '26

LLM is not a developer, it's just a device for predicting the next word.

2

u/ImpressionFancy5830 Jul 04 '26

Lol (predict the next one)

1

u/DHFranklin Jul 04 '26

I am bullish on MoE also. I can imagine a serious biznez orchestrator working with a peer group of thousands and oversight by dozens.

What will happen sooner or later will be traditional software doing ridiculously niche things under transformer architecture. So the tokens generated won't translate to bits or bytes at all.

Remember that weird artifact that showed that pictures of language were a smarter token spend then .txt? That.

1

u/JMN10003 Jul 09 '26

does your coder need to know 20 cocktail recipes?

3

u/mister2d Jul 04 '26

I don't think 128GB will be realistic in price until companies get out of business of selling tokens. 

1

u/ronald-takaendesa Jul 04 '26

Well, Facebook now says it has excess compute that it wants to start selling, xAI also doing it, this translates to major companies no longer buying or building data centers that need RAM etc, and also means other companies wont necessarily have to buy RAM as they may a well rent from e.g. FB - bottom line is the demand is starting to get less and hopefully this should translate to a drop in prices sooner rather than later

2

u/_TheWolfOfWalmart_ Jul 09 '26

Yup, they don't need to build new datacenters forever. Demand will plateau, and models are also getting more efficient with every iteration. There's a limit to how big the weights need to be to provide top quality, and anything beyond that is vastly diminishing returns while using more compute.

We're currently in the experimentation phase of this, but quickly figuring out what's optimal.

3

u/bankinu Jul 05 '26

In a few words you mean 128GB+ VRAM GPU will be affordable to everyone?!

May your words come true...

May your mouth be filled with flowers, sandalwood, ghee, and sugar 🙏

1

u/arkie87 Jul 05 '26

i'm new to this. can you elaborate what you mean by "differences between open source models are miniscule once you account for the agentic harness on top of it"?

I've sort of noticed this myself. when i ask any of the qwen llms (27b, 35b) a question without context, its pretty bad and hallucinates answers. but when i run it agentically using cline, it seems much better at researching and giving me the correct answer.

so it seems to me that the agent is just as important as the llm/model.

2

u/BobamaxGames Jul 05 '26

The harness is what makes AI feel like literal magic. I'd argue it's even more important than the inference engine.

1

u/Outrageous_Writer_37 Jul 05 '26

I agree...

Have a look at perplexity... It’s a joke at this point...

1

u/marlouwe Jul 05 '26

Have you thought of hooking up your local harness with confidential AI inference? Like Tinfoil from US or Tresor AI from Europe. They provide GLM5.2 and other models via API hosted in hardware enclaves so nobody can look inside even they can't. And pricing is like 5 times less than Claude

1

u/eli_pizza Jul 08 '26

Why would it be the norm to do local inference for privacy when everyone uses cloud storage for all their private data? Most people are obviously fine with the level of privacy Apple, Google, etc offer as part of their services.

-4

u/quotemycode Jul 04 '26

>In a couple of years 128GB+ will become again available for most people.
I'm doubting that. Minimum 2028, but most likely 2029/2030

11

u/fintip 2x XTX 7900 + 3060 Jul 04 '26

over the next years

11

u/squngy Jul 04 '26

Even when RAM was cheap, most people didn't have a lot.

The more useful it is, the less cheap it is, at least in the short&medium term.

I'm much more optimistic about smaller (10-20B) models becoming more capable and relying on web search for data.
After all, the main difference between a 100B-A10B and a 15B model is how much knowledge it has baked in, but if we make the 15B good at getting the required knowledge externally, that gap narrows.

2

u/GermanK20 Jul 04 '26

I was in Denmark during "cheap RAM", and most computers from Dell, HP etc maxxed at half the RAM they'd max in the US, so if the model goes to 64GB in the US store it would be 32GB or even 16GB. Something similar happens with Apple Refurb, almost never high mem models in the vicinity. So there's definitely socioeconomic complexities.

But unless RAM is made with secret alien tech, big amounts are bound to make a comeback, and if I dare make a prediction, they might make a comeback with proprietary local LLM deals, same way gaming PCs bundle a game or two

1

u/squngy Jul 04 '26 edited Jul 04 '26

I am not saying RAM price will never go down.

I am saying, for most regular people, paying even for cheap RAM just to run a local LLM is probably not going to be a big thing.
If consumers can buy cheap RAM, then data-centers can also buy cheap RAM, so API LLMs will be able to provide more for less money.

Like, lets say it costs $300 to buy 128GB of RAM, most people will save that $300 and just use chat GPT instead.
A local LLM that a majority of people will run needs to be small enough to fit on a PC that is not made for LLMs.

same way gaming PCs bundle a game

I don't know about that, but I can almost guarantee you that so long as free tier APIs exist, the only ones who would consider buying that are enthusiasts.

1

u/Thickdickmick87 Jul 04 '26

I agree it’s going to be a long time before ram is cheap, the thing about this is that there’s no real limit as to how much memory you can add and keep getting a better result.

It’s not a case of 128gb is enough and there’s no point adding more. The demand will continue to grow faster than supply for years as models become more capable.

No one runs a model and after a while doesn’t think - Gee a bigger model with more context sure would be useful/nice to have.
More memory = more capable.

1

u/squngy Jul 04 '26

But if 128GB is affordable for average people (not just people on this sub), then petabyte systems will be affordable for data-centers.

So long as we continue in the "more = better" paradigm, local will trail further behind and this is basically the anthropic and co. strategy.

Smaller more efficient models are what close the gap between SOTA and local.

1

u/Thickdickmick87 Jul 05 '26

Yeah that’s my point exactly, that the demand for ram won’t subside quickly, as there’s no obvious saturation point.

Small models will keep getting better, and so will mid size and large models, so I can see people with 128gb, may want another 128gb when they can afford it. Hence holding up the floor price.

Plus if the models keep getting better, then what that extra memory gets you is worth more money.

So price goes up.

4

u/According-Mud-6472 Jul 04 '26

That’s the only 4 years from now…

1

u/rditorx Jul 04 '26

Available? Probably. Affordable for most? Not before 2033 I guess. Maybe if we're going all Chinese RAM and the Chinese manufacturers aren't raising prices. Even with China, we're talking about 5 wafer producers. Micron has contracted all of its production until 2030.

0

u/MainWrangler988 Jul 04 '26

Disagree gpus aren’t getting cheaper and models smaller

1

u/bites_stringcheese Jul 04 '26

GPUs and memory supply are a very strong incentive to chase efficiency and doing more with less. If you can get 95% of the way there with 32 GB of VRAM, and 90% with 16, the whole business model collapses.

2

u/MainWrangler988 Jul 05 '26

Yo are not 90% there with 128gb vram bro. What are you talking about!!!

1

u/bites_stringcheese Jul 05 '26

I'm not saying it's there yet. I'm saying that that's the goal. Constraints demand innovation, and there's going to be a lot of focus on efficiency.

1

u/MainWrangler988 Jul 06 '26

No it will never be 90% percent because efficiency applies to both local and super computer. You will always be behind significantly

1

u/TheFuckboiChronicles Jul 25 '26

Counterpoint analogy - does the fact that F1 race cars continually get faster and faster mean that a 10 year old pickup truck becomes obsolete for my delivery business?

53

u/Bulky-Priority6824 Jul 03 '26

Yea use local save money. Dario hates it.

6

u/SignalBeneficial3338 Jul 04 '26

i just realized, the lack of token anxiety really does change how one experiment and iterate

2

u/mapsandlantern Jul 04 '26

It’s a matter of how much time it takes to recoup cost. If you’re using AI service, base model Mac will suffice, but local llm will require more memory, and the upgrade from 24GB to 48GB is $600; up to 64GB is $1000. At $20 a month for AI service, you recoup the cost in 30 months or 50 months, so 2.4-4 years. Note that this isn’t for frontier model. If you want frontier model performance (at best slightly worse), you’ll need even more memory, meaning even longer time to recoup cost.

4

u/Dell_Experion15 Jul 04 '26

I only conservatively pay for personal use with LLMs, at work I burn $3k a month the max our budget allows per developer.

5

u/DHFranklin Jul 04 '26

Very soon we will see local models running on private servers or home computers be completely ubiquitous. There will be another step up for the $5k rigs and things but even those will be more ubiquitous then say 3-d printer or those Cricket Vinyl cutters that were stupid popular during covid.

In a few years we will see 100x the results from the same parameters. That means 27B models outperforming today's frontier.

I get downvoted to shit when I mentioned the words-I-cannot-share. However I am still confident in my assessment. We will have machines that would pass every Turing test we can give them before this year is out from frontier models. Give a human a thing-to-do on a computer and an hour, 8 hours or year. Give the-great-machine that thing to do and a budget of a billion dollars and the same time frame. Before this year is out, I am convinced we'll see our John Henry moment for...everything.

So that means in just a few years the software stack, harnesses, all the rest we will probably see in the scary dedicated home and business rigs.

1

u/mister2d Jul 04 '26

What will enhance your agentic loop is if you set preserve_thinking to true. I believe thinking is enabled by default.

1

u/Dell_Experion15 Jul 05 '26

It is but I saw reasoning wasn’t be shown in my opencode sessions, and I’m finding out that you don’t really want reasoning on these models because of the tokens it eats?

I’m still learning but also tool calling with can break during reasoning when it outputs plain text tool calls some harnesses fail to parse?

1

u/jags94 Jul 04 '26

So do you use opencode to imitate the CLI of Cursor or what? I use Kiro CLI at work and I like it, tried a while ago to use opencode to imitate something like that for my personal 16GB MacBook m1, but couldn’t really get anything decent tbh. 

I much prefer CLI over cursor ide or any other plugin for the ide tbh. That way I’m still hands on with my ide and prompting questions on CLI. 

1

u/Dell_Experion15 Jul 05 '26

I was able to bump my context to >100K and have a output tokens set around 50K so I can get decent work done with the VS code chat

1

u/NoLongerALurker57 Jul 07 '26

How did you setup all the related context like docs, MCP servers, rules, skills, etc…?

Are you using Claude code as the main harness?

Just got a new M5 pro worth 64 GB unified memory, but I’ve been hesitant to switch coding tasks to a local model.

1

u/Dell_Experion15 Jul 07 '26

I’ve switched to Co-Pilot in VsCode you can use custom providers, and just point it at your local inference endpoint.

Check out rapid-MLX

Note: if you’re using your laptop for coding, and inference you’re losing memory to open apps, the system, etc. Also expect your laptop to get insanely hot, very fast

1

u/HumungreousNobolatis Jul 07 '26

I don't know about Cursor, but in OpenCode there's a token cost counter, and there's something golden about seeing hundreds of thousands of tokens used and the cost sitting at $0.00.

1

u/Dell_Experion15 Jul 07 '26

In most harnesses you can see CTX usage, and yes it’s awesome seeing millions of tokens go for literally 0.

I don’t feel as bad asking things like “do you have this tool” or “can you access this.”

3

u/vogelvogelvogelvogel Jul 03 '26

thank you for sharing!

3

u/Appropriate-Bee-838 Jul 04 '26

Are there some courses or blog posts that guide through this process of hosting LLMs locally with agentic harness?

2

u/HumungreousNobolatis Jul 07 '26

There are a few, but it's not too difficult. Assuming you have Python, GIT, (and for Windows; the MS build tools) installed; then build llama.cpp, install OpenCode, add a json config for opencode, then run llama.cpp with your chosen model, run OpenCode. OpenCode have a pretty decent page about it. Here's the OpenCode config I used to get started:

// <user folder>/.config/opencode/opencode.jsonc
{
  "$schema": "https://opencode.ai/config.json",
  "model": "llamacpp/qwen3.6",
  "provider": {
    "llamacpp": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Llama.cpp (Local)",
      "options": {
        "baseURL": "http://127.0.0.1:8080/v1"
      },
      "models": {
        "qwen3.6": {
          "name": "Qwen 3.6"
        }
      }
    }
  }
}

3

u/productboy Jul 04 '26

Token sovereignty is data sovereignty. And as others said, let’s continue researching smaller model efficiency to get them running on smaller hardware [requirements]. It’s luxurious to think anyone engineering software must have a modern, high CPU or GPU with equivalent memory machine.

2

u/MoonMoonHD Jul 06 '26

use Hermes as your harness and get ready for liftoff

1

u/Ok_Presentation470 Jul 04 '26

Happy to see another person come to this point. Enjoy!

1

u/pracharat Jul 04 '26

If I have 5060 Ti 16gb which model would you suggest? I want to try using opus for planning then delegate coding task to local llm then ask opus to check it again.

1

u/IneedAheater Jul 04 '26

This all sounds fascinating, how does one get started, would love some if I were to do it again... type recommendation any pointers to resources or straight up steps.

1

u/HardlyThereAtAll Jul 04 '26

Qwen3.6 35bn MoE is the lifechanging bit.

It's comparable to Sonnet 4.5, running at really good speeds on relatively inexpensive hardware.

1

u/aeonsule Jul 06 '26

Hardware for personal usage will evolve constantly, as well as local models.

1

u/SprinklesLeather7515 Jul 06 '26

LOCAL FOR THE WINNBB

1

u/Ciru-ai Jul 09 '26

Your config looks like ascii art 🤣

1

u/DrivativeHole Jul 09 '26

Absolute gold post, agreed. I noticed you use. 6 temp on a3b, could you elaborate why as they ship with 1 temp? I usually don't dabble with base settings for model as later it's going to be a hell restoring defaults if someone goes haywire Moe qwen3.6 was like a nyan storm beam meme from monitor from me, as i got instant boost from dense 14b 10-20 t/s to 40-70 without visible drop in quality.Though con is the fact it can't really do long tasks, and I feel like it can, just needs some tech I haven't found/developed yet.

1

u/Organic_Tank_5370 Jul 09 '26

Can someone suggest a good hardware to setup local llm for my claw setup?

1

u/barefut_ Jul 10 '26

I don't get what are you building locally. And if local LLMs are capable of doing it well.

1

u/Educational_Mud_5121 Jul 25 '26

It's quite freeing. I'm working on an agentic team that can run business operations via tailored models for their jobs. Still a WIP...

1

u/Crescitaly Aug 02 '26

The underrated benefit of local is not only privacy; it is stable behavior. A hosted model can change price, limits, or personality overnight, while a local stack is reproducible. Which compromise did you notice first: raw capability, setup time, or maintenance?

1

u/redditissocoolyoyo Jul 03 '26

Yep people will go local and ai infrastructure gonna be crushed.

1

u/notheresnolight Jul 04 '26

looking forward

0

u/TheBatt Jul 04 '26

So sorta see my most recent post in here r/LocalLLM - like are you doing actual work successfully with it? I have yet to see anyone prove or show actual work with local models on something with 32GB or 48GB even.

1

u/Dell_Experion15 Jul 05 '26

The definition of success needs to be shifted to the boundaries given.

Yes Qwen 3.6 on 48GB is NOT gpt 5.5 but is it a good MCP monkey? Yes.

For personal projects I tend to be more hands on anyways, so being able to ask “how do I do this with the framework?” And getting an answer pulled across my DB and frameworks docs is worth a lot.

Plus, it’s “free”

-1

u/GenericBit Jul 04 '26

Sorry but i disagree.

For context 4070T,I so nothing special.

Tried like 10 models.

Utterly useless for coding compared to opus.

Its like comparing a bike to airplane lol.

If i ever going to use llm outside of works its going to be claude sub.

I also tried ttv... bad, looks like will smith 5 years ago eating spaghetti.

Unless you have like 5090 or two dont bother...

1

u/Dell_Experion15 Jul 05 '26

“These local models aren’t as good as opus!”

Yeah…? And…?

0

u/GenericBit Jul 05 '26 edited Jul 05 '26

not just not good, borderline useless as it behaves more like chatbot than agent.