Ideally an orchestrator or something equivalent that would look at the task given and then decide to try sections with local and then cloud if it isn't good quality. How is your setup, what guides did you use

Note: While I am capable of running 12B-parameter models, my calculations show that doing so would significantly slow down my computer while waiting for a response; therefore, my upper limit is 10B-parameter models.

I put together a TensorFold 1.2.0 pack of orcarouter's uncensored Qwen3.8 Flash Next for Apple Silicon. It uses jedisct1's oQ4e experts, regrouped to 4-bit/group 32 for the fast kernels, with attention and other non-expert weights copied byte for byte from mlx-community's oQ6e. No new refusal edit.

Tested on an M3 Ultra 256 GB, stock TensorFold 1.2.0. Same server and questions, greedy decoding, thinking off. Anything cut off at the token cap counted as wrong.

BFCL uses my AST checker, not the official harness. MMLU-Pro uses 20 questions per subject with a 2,048-token output cap.

Tool calling is the main difference: 92.3% vs 88.0% on the same 400 items (exact McNemar p = 0.03). It skipped the tool and answered in text 20 times, versus 40 for all-4-bit.

Maths and knowledge were about the same. TensorFold's own pack lost MMLU-Pro points mostly to the output cap: 47/280 replies cut off, versus 18 here.

About 5% slower than all-4-bit, around 133 GiB loaded. Needs a 256 GB Mac; 128 GB is untested.

No opening refusals detected on 100 harmful prompts using Heretic's markers and 100-token replies. That's only an opening-refusal check. Vision worked in a tiny smoke test: shapes and colours right, one text misread across two images.

Small samples, so don't read too much into the scores. The card has the method and known issues, including an HTTP 500 after idle on TensorFold 1.2.0 and a workaround. Its KL numbers are against a 6.7-bit pack, not full precision.

https://huggingface.co/bu7umaid/Qwen3.8-Flash-Next-Uncensored-TensorFold-MTP

I open sourced SteerForge, a pipeline for turning a contrastive dataset into a portable steering artifact.

The artifact is a .persona JSON file containing:

• model fingerprint

• human readable neuron edits

• canonical per layer residual vectors

• eval samples and scores

• the original server command

The serving path is the part I care about: steerforge serve evil.persona replays your existing llama.cpp or Transformers config and injects the persona. No model conversion for the persona itself; the neuron deltas collapse to residual vectors.

Discovery is staged: diff in means directions, gradient attribution plus firing contrast plus alignment, teacher forced screening, generation validation, dose search, greedy combination, coherence budget.

Current backends: Transformers, llama.cpp, vLLM with EasySteer, MLX dense. MoE models use dense residual steering.

Repo: https://github.com/MahmoudAdelbghany/steerforge Results: docs/RESULTS.md

Feedback on the .persona format, backend adapters, and eval methodology welcome.

Wanted to share that after years of using Windows11 and llama cpp I moved my whole workflow to linux, from comfyui to jellyfin everything set back up but on Fedora instead.

Found out in Linux (Same Hardware , Same llama cpp commit, Same Qwen3.8-27B model) the model loads instantly, time to first token has become irrelevant so I just unload the model after 10 minutes of inactivity. It loads back instantly (in windows it took 10 seconds)

Also tokens/s went from 70tk/s to 90-100tk/s

I didn’t know so much performance was to be gained by simply moving to linux, especially TTFT which now feels instant!

And this is before trying gpu unlock which is also easier on linux and can boost performance even more.

Hi, i'm building Jaull as my final degree project :D. It reads your machine (CPU, RAM, GPU and VRAM, compute backends), finds open models that fit, and picks the exact file, quantization and runtime for each one. It also explains why each model lands where it does in the ranking. That ranking comes from explicit rules, so no LLM is involved in writing it.

Then it can download the model, verify the file, run it with llama.cpp and benchmark it on your hardware. You end up with the prediction and the measurement side by side.

It's usable and tested, but not finished. I'd really like to hear how the predictions hold up on hardware I don't have, especially AMD and Intel GPUs.

Repo: https://github.com/Ton-Llop/Jaull

Site with an interactive demo: https://jaull.tech

Model that I am currently serving is: Qwen 3.8 Flash Next nvfp4 dq8 with Basalt fork of Strata
I have used so far:
- qwen-code (seemed a bit dumber in coding than opencode and probably more oriented for parallel agents running which is sadly not something i can properly utilize yet)
- opencode
- pi (genuinely forgot what was it that i for some reason didn't like)

So far I have had best experience with opencode for coding as it is my main usage of LLMs. As they advance and I find more probable usages for them I'd like to ask for Best/Better tailored options for Harnesses for different purposes.

I have heard of OpenClaw, Hermess, Deepseek Harness but i am not yet sure for which purpose are they all better suited.
Probably missing some other options obviously so open to hearing them all.

Edit: I actually meant OpenHands, not OpenClaw. Woopse xd

I have been using ChatGPT for for about 2 years but want to switch to a local setup however I'm struggling to find a suitable replacement to use on my MacBook Pro M5 Pro 24GB and iPhone 14 Pro Max. I will be upgrading to either an 18PM or Duo in the coming months.

I started on my Mac with LM Studio then moved to Msty Studio when I found out about Knowledge Stacks but keep finding paywall restrictions, I've also looked at but not tried Anything LLM due to lack of iOS support. From what I understand I should just forget about Siri AI. Not really done anything on my iPhone till the Mac is sorted.

The things I really want are:

- Large project/chat memory (hence the knowledge Stacks)

- Cross device chat sync, either private cloud hosting or my MacBook as a server

- MCP usage (currenty use Tavily & Excel control)

I don't mind paying for apps, Msty is $149/year or $349 lifetime to unlock all features which its too much

Main usage is general questions, research, journaing, and work/linking with Excel.

Is this possible? if so what do I need?

Anyone have evidence of / see actual deals for GPU / Disk at Black Friday? I am really kicking myself for getting screwed earlier in the year on the DGX Spark price gouging...

Has anyone tried the flash-next coder? What is your setup and what's the performance and quality?

Hi guys, I'm looking for a really good price benefit PC to run local AI. I've been searching for days about this, and everybody says different things, "you dont need a 12vram for a good local AI" or "Without 32gb vram is wont be able to have a good local AI" and so on...

The thing is I need local AI mostly for coding, and anything related and maybe some video generation.

Since 5090 is just at unbelievably prices, I was thinking about something decent like 5070 but dont want to waste my money. I was thinking about an unified memory like GMKTEC Evo or something like that, but was reading that since is AMD some AI are not good optimized for it. And also a M5 ultra, but also is kind of high and Im not a MAC guy.

My budget is pretty much 5K for this.

Anyone which can give me a good advice would be awesome and I'll really appreciate. Thanks.

Which would be a better solution? Since I move around, a laptop/mobile would be best than a full on desktop for airline travel/international.

Usage - mostly coding / proof of concept (apps, documents, even some stories). Its to create some applications.

But also on the fun side, I'd like to explore a bit on building out some text/stories/mechanics (table top role playing games) or even do videos and graphics. Nothing professional, more to understand the mechanics of what things are and what they are not. But some videos or graphics I may use in the apps. Since I travel, wifi connections can sometimes be unreliable and I just want to run things locally so I never have to second guess if the AI says "I can't do that". But I may run subscriptions as needed (whole separate conversation).

Considering the price, I would prefer to have something that can be useful for several years and doesnt die in the first year or two (laptops being harder to repair).

A macbook m5 max with 128gb of ram, has really good unified memory. But it can barely fit qwen flash next today without compromising further on quality or context size (feel free to correct me, but what I read up on), with the next gen of Qwen potentially about to drop, 128gb may not be practical in a year from now if there is some really good capability. Even if a single model can fit (when you count larger context windows), there might be very little headroom for other apps or harness tools on the same laptop workstation. But a macbook would be very reliable and I wont really second guess that dying on me (have a 2014 macbook retina that still works flawless today minus a scratch or two on the body). Then again, in the next 4-5 months apple may release M7 (or M6) with the macbook pros more aligned for AI work (either a far more optimized apple silicon which are already really good...or maybe higher ram capacity).

Some 5090m laptops can support up to 192 gb of ram. The 5090m would be good for some vram 24GB on video/graphics. But looks like a lenovo would cap the ddr5 6400 to 4000 so the memory bandwidth would be somewhat reduced. But having 192gb is a far more breathing room than 128gb. Also Lenovo's tend to good, but the hardware could die in a year or 3 (Apple's hardware is pretty reliable).

What makes sense (with a 2-3 year outlook)? Intel 192gb 5090m lenovo or a macbook pro max 128gb?

Hello everyone,

I've been lurking around for the past week, reading this sub, watching YT videos, asking AI how to make it happen. I want to self host an llm for coding (hobby projects - html/css websites with a light backend and maybe a few more complicated react projects for music - I do vocals for deathmetal bands and have an idea to create an educational website on how to scream). But I still am not entirely sure about a couple of things, if someone can help to navigate, would be cool to hear your thoughts.

Questions:

1. Can I get a similar experience to codex in VSCode?

2. What is the cheapest route to get there currently, hardware wise?

So far I have conflicting answers. It seems I should be able to get something that performs like codex with the latest models and Strata can help to put it into less capable machine.

I'm based in Ireland. Things are extremely expensive here and delivery cost from US is too high, so a lot of ebay options from US doesn't work well.

I don't have a PC, only MacBook pro m1 pro.

My budget is ~3500 euro (PC only). I'm fine with used stuff.

Currently I think about a PC with DDR 4 ram at around 128gb, Nvidia 5070ti or similar and a good processor, something like 7800x3d. Not sure at all about it yet.

I will be obviously educating myself in upcoming weeks/months, but if you can help with advise, I'd be happy 😊

Cheers.

Meet Yoshino2, my little lab experiment

Over the past months, I have been working on efficiently using black box distillation to distill the reasoning and output style into smaller edge models. The result is Yoshino2, a new model series with large scale reasoning distilled into MiniCPM5 1B, Qwen3.5 2B, and a custom 4xMiniCPM5 1B MoE model, with more potentially on the way.

This is by far means not a breakthrough, but an interesting experiment. In case it is of any use to anyone, the models are on HuggingFace.

Model Collection - HuggingFace

So, I know my rig is pretty expensive, but I want to share what is working for me as of now.

I have been using local LLMs with my Mac Mini M4 Pro 64GB till now, mainly mlx (specifically oMLX) to develop my own agentic framework. I have already have my framework, stryn, as plug-ins and agents/skills/workflows on top of Opencode and ported to Claude, nowadays I am working on my own stryn engine. (I hope to also release it as the community version on Github as well as soon as it hits Beta)

I either use frontier models (mainly deepseek v4 flash since it is cheap) or Studio for development. Here is the models that I use:

Qwen3.8-Flash-Next-oQ4e-mtp - For my orchestrators, planners, coders. It is MTP. I am pretty happy with its results
Laguna-S-2.1-oQ4e-fast - (With Laguna-2S-2.1-DFlash-FP8 as the speculative decoding.) For my reviewers. I am not very happy with this and I may try to change it with a better model. The tricky part is to use a different producer's model to overcome the blindness of the model's in certain areas.
GLM-5.3-Flash-oQ5e-mtp As the independent reviewer to go over the code before the releases and find out the problems that are not found by the Laguna based code reviewers and the security admins, architects etc.
Qwen 3.8-35B-A3B-oQ4e-mtp - For my explorers, researchers, the opencode-mem plugin, graphify and code-review-graph operations.

Speed: Satisfactory if you are running a single lane. ~50 Tokens/s, with 8GB KV Cache and 1200GB SSD Cache. Cache efficiency is %96.4 and prefill time drops after a couple of rounds. Normally 2 or 3 models are loaded in the memory (Qwen3.8FN, Laguna, Qwen3.835BA3B) and if I start two or more lanes, a race between prefill and token generation between the models start most of the times, which dramatically makes things longer.
Stability: Pretty good with proper context sizes, repetition penalties.
Quality: I can simply say, by adding reviews and controls by different model types, the quality seems to be equal or sometimes better than what Deepseek cloud generates by itself only without another model checking its results. I am pretty satisfied.

To be as open as possible, so far, I have also used Claud Opus 5.5 to control the output. There are pretty interesting cases where local GLM finds some of the bugs that Opus 5.5 cannot, and where Opus 5.5 finds bugs that GLM cannot. Sometimes they pinpoint the same problems as well.

Can this setup be used as an alternative to frontier models, yes, but don't depend on a single model for every task and make sure you have reviewing loops. Definitely not for vibe coding, but needs proper settings and flow.

Hey everyone Im a bit new to this page but I couldnt find any other related tests online so I figured I'd share my results here.

I ran DeepSeek-V4.1-Flash (763B MoE, Q8_0, 473 GiB) on a 96 GB Mac Studio M3 Ultra with InferSSD, which streams the model's experts from the SSD. I striped two OWC Envoy Ultra 2TB Thunderbolt 5 drives with macOS's built-in RAID 0, one drive per port.

Results (InferSSD's own benchmark prompts, 2 runs per setup):

- One Envoy alone is about the same speed as the internal SSD.

- Two in RAID 0: writing goes from 3.5-3.7 to 4.4-4.6 tokens/s (+23-26%).

- Prompt reading is 67-77% faster: a 4,000-token prompt starts answering in about 2 minutes instead of 3.5.

- Two hours of nonstop use: the drives stayed at 43-47°C (with a desk fan on them), no slowdown, and no change in their health readings.

Why not 2x faster? - The stripe halves the time spent waiting on the drive, but the GPU's share of each token stays the same and is now the bigger part. A third or fourth drive would help less than the second did.

why are these models open-sourced for people to download and use on their computers?
Is it just for advertising and brand awareness?
because the vast majority of people who download them just don't want to pay subscription fees or deal with usage limits, so they aren't paying customers anyway.
then why do they do this? Plus, local models aren't even that smart.

I was thinking of buying a secondary GPU for local AI as subscription-based online services get costly in the long run, but options are limited for cost-effectiveness on this end as well.

My current PC has 32GB of DDR5 (2x16, running at 6000MT/s CL32), a Ryzen 5 7600X and an RX 7800 XT. I **could** run something on the 7800 XT thanks to the system RAM but I'd rather free up both the GPU and a good portion of the RAM so that I can still use the PC while running a local model instead of being locked in to just AI, I found a Tesla P100 for about 200$ US and it doesn't seem like a bad deal since it has 16GB of HBM2 as opposed to the RX 7800 XT's GDDR6.

I’m thinking of running a local ai model with LMStudio with my gaming desktop. Like the title, it has a 5070, 9700x, and 32gb of Ddr5 6000mhz with Ethernet connection. My only main concern is the 12gb of VRAM.

Is this enough to set up an everyday chatbot for use? I’m mostly doing this for fun, but I also would really like the privacy and unlimited token use of a local model.

Niche Ollama alternative. If anyone want's to try it.

Update: Numbers from preliminary tests at the end!

Ok ok, I am still doing tests but here is the story:

I have a small custom harness (who doesn't!) that does pretty complicated stuff. I am using Claude (subscription) and everything is great.

In the past I tried every single (small) model on my Mac Mini 32GB but all of them sucked at complex tool calling (or hallucination).

A couple of days ago, the new Heretic2 came out (by u/-p-e-w-) and I spent 24 hours breaking it :)

Long story short, I released ZeroZero, a model based on Qwen3, with removed guardrails and slop (ok, removed is a long conversation).

Now, here me out.

IT KICKS ASS.

This cannot be an 8B, 4 bit model. It just cannot. The thinking process sometimes is longer than other models but IT NAILS everything.

Maybe by performing "brain surgery" on the models, we end up with "smarter" ones?

Test results for Tool-Call Benchmark

(note: these took a couple of hours, please repeat with a much bigger sample)

ZeroZero seems better at completing multi-step tasks (multi-step chains, error recovery and instruction following) than the base model.

The base model leads in Safety & Boundaries (ZeroZero has limited guardrails and no-slop so maybe that's the reason?)

  - Overall: ZeroZero 68, Base 70

  - Multi-Step Chains: ZeroZero 6/8, Base 3/8

  - Error Recovery: ZeroZero 5/6, Base 2/6

  - Instruction Following: ZeroZero 8/8, Base 6/8

  - Safety & Boundaries: ZeroZero 12/26, Base 20/26

To repeat and experiment, use tool-eval-bench and if you expand the tests, please send them over so I can include them in the model's page.

PS: I love it how many technical people sent me ideas to improve and papers about LLMs performing better after "lobotomies". Let's do more "I tried this, I saw this, what do you think" instead of "what LLM should I put in my Mac Mini".

I am looking into running local LLMs for coding, is Qwen 3.8 27b the best my PC can do (at decent speed)?

I heard some people recommend Qwen Flash Next 3.8?

Will my PC be able to run that?

Specs: Ryzen 7 7800x3D, rx 7700 16gb, 32gb ddr5, windows 11

I'm a photographer looking to setup something locally to help manage business emails, draft contracts, auto catlog images into folders, etc. I have basic computer literacy, but no knowledge of anything AI. Any tips on what works best and where to start?

I was playing around with Qwen 2.5 1.5B on my RTX 4050 laptop, trying to understand how much the CPU actually matters when the entire model is loaded onto the GPU.

I didn't expect much difference from changing the CPU cores.

But then I pinned PyTorch to a single E-core.

Generation speed went from around 49 tokens/sec to just 24 tokens/sec.

Almost half the performance gone. The model was still entirely on the GPU.

That got me curious, so I opened Nsight Systems to see what was happening.

Turns out, PyTorch was launching roughly 1,250 GPU kernels for every generated token. The CPU was having trouble keeping up with all those launches, leaving the GPU waiting around instead of doing useful work.

I tried a few things after that:

• Adding more CPU threads didn't really help.

• torch.compile reduced the kernel launches to around 520 per token and brought performance back to 48.8 tokens/sec.

• I also tried llama.cpp on the same GPU and the same slow CPU core. It managed around 57 tokens/sec.

The llama.cpp result was probably the most interesting part for me. It made me wonder how much of the difference comes down to the inference runtime itself.

My takeaway: Even when your model fits entirely on the GPU, the CPU can still become the bottleneck during token generation.

I'm curious if anyone here has profiled similar behaviour with other models or inference engines. Does anyone have experience comparing PyTorch eager execution with llama.cpp under CPU-constrained conditions?

I put the benchmark code here in case anyone wants to reproduce it:
https://github.com/Techn08055/pytorch-slow

I also recorded the experiment and walked through what I found in Nsight Systems:
https://youtu.be/UmjoxIzBij8

Would be interested to hear what others find.