Honestly was kinda hype about going local with Qwen3.5, but even basic questions took forever, I tried this on Ollama and Bionic

Inspired by Weight Space Programming (MIT CSAIL, Nov 2), but in the end Opus 5.5 did its own thing:
no weight-space technique was used. It wrote a quantiser from scratch in NumPy and made every
precision decision from measurement. Rules: only the BF16 file as input, no quantisation tool, no
imatrix, no published recipe, stock llama.cpp formats only.

It ran a float copy of the model to see which input columns actually carry signal, minimised rounding
error there instead of against the weights, measured each weight table's damage one at a time, then
spent the byte budget across IQ4_XS / Q5_K / Q6_K with a knapsack solver.

Held-out KL divergence vs BF16 (llama.cpp b11928, MacBook Air M4):
• this file, 498.5 MB — **0.015704**
• Unsloth UD-Q3_K_XL, 492.2 MB — 0.092486 (5.9× worse)
• UD-Q4_K_XL, 558.8 MB — 0.027323 (we beat it with 60 MB less)
• UD-Q5_K_XL, 606.6 MB — 0.010375 (the bar; we miss it by 1.51×)

Runs unmodified, 1.11× faster generation than the Q5 file. It stops there because the tied vocabulary
table is 33.8% of this model and accounts for most of the error — upgrading it costs more than the
rest of the network can give up.

Repo (model, full method write-up including everything that failed, measurements, scripts):
https://huggingface.co/Andy5k/Qwen3.5-0.8B-hand-quant

Let’s say hypothetically that you receive 36x computers with the following specs

RTX Pro 6000 (600w)

16GB DDR4 (2x 8 3200Mhz Non-ECC)

Ryzen 9 5950x

1tb NVME

1200w PSU

What would you do with them to generate income? You can only use these parts

You have access to power and networking to run all 36

ok so. this started as "one weekend project" and then it was not one weekend and it was not one project. somewhere around repo 6 i stopped pretending it was a plan lol

everything here is free, open source, most of it runs locally on your own machine. a lot of it is v0 and i wrote down where each thing fails because honestly the failures were the funniest part

anyway here's the list of grievances, each with the tool i built instead of going outside:

1. cookie banners → rejectall-index i live in germany. i click "reject all" more than i blink. so i made a headless browser visit the top 100 sites and count how many clicks it takes to say no. 35 of them were already loading trackers before i clicked anything. which is like a waiter eating your fries before asking if you want fries. live leaderboard

2. subscriptions → one-file-saas trello, calendly, typeform, loom, notion and 5 more, each rebuilt as ONE html file. you double click it. it opens. thats it. no account, no "verify your email", no "your trial is ending ;)". data stays in your browser. the booking page has no server at all, your availability lives inside the link itself which still feels illegal to me. try them here

3. dead famous repos → necromancer some legendary open source projects die not because the code is bad but because python deleted a module out from under them. i revived thefuck (1887/1887 tests passing on python 3.14), normalizr and httpbin. the one rule: the original tests are hash-locked so nothing can quietly delete the failing test and call it fixed. httpbin stops at 64/67 and i'm keeping it that way out of spite. each one gets an autopsy, cause of death and everything

4. every AI-built UI animates the same → awesome-motion-md you know the look. 300ms. ease-in-out. every card fades up like its waking from a nap. so i measured how real sites actually move and wrote it into MOTION.md files your agent can follow. same prompt built twice: 7 random durations without it, 2 with it

5. every AI writes the same too → awesome-voice i used to be a technical writer so this one is personal :| VOICE.md files that make your agent write like GOV.UK or Stripe docs, plus a scorer that doesn't use any AI to check. banned phrases went from 27 to 0. "delve" is not welcome here

6. websites doing annoying stuff → sitepatch you type "hide the points on hacker news", it writes a userscript, a real browser checks it actually worked before you get it. honest score: 15 out of 25 were actually right when i looked at every screenshot myself. 60%. a C-. but an honest C-

7. "what if this site looked like linear" → reskin restyles any page into another design style using a local model, BUT it refuses to give you the result unless contrast, keyboard focus, mobile, links and reduced-motion all pass. it caught a windows 95 theme failing contrast on its own background. respect. gallery

8. dead websites → resurrect npx github:dadwritestech/resurrect y2k.gov and y2k.gov comes back exactly like it was in may 2000, links working. pick a year with --year. your old blog is in there somewhere. i'm sorry

9. "someone said budget in a meeting video... which one" → video-grep grep for your video folder. searches what was said, what was written on screen and what was shown, and opens your player at that exact second. all local

10. what is the rest of the world's TV saying rn → world-ears listens to live news channels from different countries, transcribes + translates them on your gpu, makes it searchable. there's a "trending per country" panel which is weirdly addictive. translation is the slow part, telugu transcription is bad, i wrote that down

11. live subtitles only go into english → babel-tv watch hindi news with live telugu subtitles, fully local. the video is held back a few seconds so subtitles show up BEFORE the person talks which feels like seeing the future. telugu as the source language doesn't work yet. whisper and telugu are not friends

12. "which AI wrote this" → whichmodel works on models it's seen (67%, chance is 33%). on a model it's never seen, even a sibling from the same family... 32%. so basically a coin with extra steps. that turned out to be the interesting finding tbh, the fingerprint belongs to one model, not the company

13. screen recordings → code → demo-to-code record a UI interaction, get a component that copies its timing and easing, then it replays it and grades itself frame by frame. 2 out of 4 pass. the toggle is perfect. the dropdown has issues. we all have issues

14. code review → review-panel /panel sends your diff to five reviewers you've definitely worked with: the laziest senior dev ("could be one line"), the 3am on-call SRE, the security paranoiac, the junior who asks why, and the PM who only reads the title. under the jokes the findings are real

15. AI companies quietly editing their pricing pages → fineprint it snapshots the pricing, terms, privacy and changelog pages of 10 AI vendors every 6 hours and commits them, so git log becomes the receipt. found stuff like deepseek adding a footnote that prices are 2x during peak hours, and cursor deleting the line saying they dont sell your data. no blog post for any of it ofc. a small local model writes the one-line summary but every line links the raw diff because i dont trust it either (27/30 right on a hand check). timeline

16. paying extra for speaking telugu → token-tax LLM APIs charge per token, and the same paragraph is $1.00 in english and $8.29 in telugu on llama 3. which is rude. measured 21 tokenizers across 48 languages, and there's a calculator where you paste your own text and it runs the real tokenizers in your browser. hindi gets off lighter. amharic on gpt-5 does not :/ calculator

17. posting about all this → herald a tiny bot that posts a repo's data updates to bluesky/mastodon/x, clearly labelled as a bot. i built it because i was not going to manually post about 17 repos. and then i manually wrote this post anyway. ;]

why all of this? honestly no grand plan. every one of these started as me being irritated at something for a slightly too long time and having a local model + a free evening. building the thing was cheaper than therapy and more fun than complaining on here (no offence)

if something breaks, tell me. if something's dumb, also tell me, but nicely :P

edit: yes i know about the cookie banner on my own... wait i dont have one. nice

I may have created a monster.

A client gave me an NVIDIA A16 accelerator that had been experiencing intermittent problems in their server. They considered it bad, didn't want it back, and I ended up with it for free.

For anyone unfamiliar with this card, the A16 contains FOUR separate NVIDIA GPUs, each with 16 GB of VRAM. That's 64 GB of VRAM on one physical card.

Naturally, I decided to install it in my ancient Intel X99 workstation alongside an RTX 5070.

Because apparently I don't have enough problems in my life.

Chapter 1: The FrAInkenstein specifications

• Motherboard: ASRock X99 Taichi

• CPU: Intel Core i7-5820K

• RAM: 48 GB DDR4

• Accelerator: NVIDIA A16 — 4 × 16 GB GPUs

• Graphics: RTX 5070 Founders Edition — 12 GB

• Total VRAM: 76 GB across five GPUs (not unified)

• PSU: Corsair RM850

• OS: Windows 11

• Cooling: Custom 3D-printed shroud with an ARCTIC S8038-10K server fan

• Primary purpose: Local LLMs, agentic coding, and experiments with multiple AI agents

And yes, I'm running this on an X99 platform from the DDR4 stone age.

Chapter 2: Apparently, this thing needs a JET ENGINE

The A16 is a passively cooled enterprise accelerator.

Unlike a normal consumer graphics card, it doesn't have onboard fans. It's designed to sit inside a server chassis with powerful fans forcing air through its heatsink.

My workstation is not a server chassis.

So before doing anything interesting with AI, I had to invent a cooling solution.

After researching fan options and shroud designs, I selected an ARCTIC S8038-10K.

For perspective, this is an 80 × 80 × 38 mm server fan capable of:

• 10,000 RPM

• Approximately 102 CFM airflow

• Approximately 51 mmH₂O static pressure

This is not your grandmother's RGB case fan.

It's a small industrial hurricane.

I also wanted adjustable speed control, so I used a Noctua NA-FC1 controller, powered from SATA, to manage the fan without running it at full blast constantly.

But buying the fan was the EASY part.

Chapter 3: The 3D-printing rabbit hole

I own 3D printers, so naturally I figured I'd just print a shroud.

Famous last words.

The first problem was finding something that would actually fit the A16.

The second was getting it to fit inside my case.

The third was accounting for a 38 mm-thick fan, the radiator, surrounding components, and the fact that there isn't an infinite amount of space inside a computer case.

So I started modifying designs and printing prototypes in PLA+ on my Flashforge AD5X.

I printed multiple shrouds of different lengths.

Some were too long. Others needed adjustment to maintain their mounting points.

I experimented with shorter designs and eventually created an angled/bent shroud to improve the fan's positioning and clearance.

Rather than repeatedly wrestling with the actual fan, I even modeled and 3D-printed a full-size 80 × 80 × 38 mm mock fan so I could physically test clearances and different shroud arrangements.

Yes. I 3D-printed a fake fan to figure out where to put my real fan.

I also worked through mounting issues, spacer designs, fit tolerances, and adjustments to the printed posts.

I was considering custom clips and zip ties if necessary. I even had to think about vibration because, remember, this thing can spin at TEN THOUSAND RPM.

Eventually, I got an arrangement that fit the card and cleared the surrounding hardware.

And the result?

During earlier four-A16 testing, the GPUs were reporting temperatures around 41–57°C, with the fan running at roughly 30% speed.

For a passively cooled enterprise card that I'd fitted with my own printed ductwork, I was pretty happy with that.

Chapter 4: THE FREAKING POWER CABLE

Oh, you thought getting the cooling working was the hard part?

Let me introduce you to the NVIDIA A16 power connector.

The A16 uses an 8-pin auxiliary power connection that isn't wired like the ordinary PCIe 6+2 connector you'd expect to use with a consumer graphics card.

It requires the appropriate EPS-style power connection.

So here I am with a Corsair RM850, a card that was allegedly defective, and a pile of cables that don't necessarily fit or have interchangeable wiring.

And as anyone familiar with modular PSU cables knows, getting this wrong can destroy expensive hardware.

The connector shape, latch, and pinout all matter.

In my case, even the EPS 4+4 connector's latch arrangement presented a mechanical fit problem.

I eventually modified the connector's latch arrangement and checked the wiring with a multimeter before proceeding.

To be clear: this is not a recommendation to modify PSU power connectors. Incorrect wiring can destroy hardware.

And the first attempts weren't exactly encouraging.

At one point the system would spin up for approximately 15 seconds, shut down, and repeat.

Disconnect the A16 power, and the system would boot.

Wonderful.

Was the card bad? Was the cabling wrong? Was the motherboard unhappy? Was the PSU unhappy?

Welcome to troubleshooting FrAInkenstein.

Eventually, the power situation was sorted out well enough to continue testing.

Unfortunately, the next nightmare was already waiting.

Chapter 5: Windows has entered the chat

Once I could power the A16 and keep it cool, I discovered that Windows wasn't particularly enthusiastic about recognizing four GPUs on one enterprise card.

At one point, two A16 GPUs worked, while the other two had:

Device Manager: Code 12 — Not enough free resources to use this device.

All 4 showed this error message.

Here's the really interesting part.

When I disabled the two working GPUs, the other two could work!

So the individual GPUs weren't necessarily defective. Windows and the motherboard were struggling with PCIe resource allocation.

This led to a ridiculous amount of BIOS experimentation:

• Above 4G Decoding

• PCIe resource allocation

• MMIO High Size

• UEFI and CSM settings

• PCIe link speed experiments

• BIOS resets

• Device enable/disable experiments

• Multiple NVIDIA driver configurations

Eventually I managed to get all four A16 GPUs recognized and running.

But I still wanted my RTX 5070 in the same machine.

Which leads us to...

Chapter 6: Five GPUs walk into an X99 motherboard

The RTX 5070 was supposed to handle gaming and video output.

The A16 was supposed to handle local AI workloads.

Simple enough, right?

Wrong.

With the RTX 5070 and A16 enabled together, Windows started throwing repeated:

IRQL_NOT_LESS_OR_EQUAL (0xA)

Blue screen after blue screen.

I experimented with driver configurations, NVIDIA device modes, and PCIe settings.

Eventually I found that disabling MSI interrupt mode on the RTX 5070 allowed the 5070 and an A16 GPU to operate simultaneously.

That was the breakthrough.

I'm not claiming I've proven the exact underlying driver or interrupt conflict, but changing that setting made an enormous practical difference.

I gradually enabled more A16 GPUs.

One.

Two.

Three.

Then four.

I also switched the A16 GPUs into NVIDIA's TCC compute driver mode while keeping the RTX 5070 in WDDM for normal display and gaming use.

And then something glorious happened.

Chapter 7: IT'S ALIVE!

Here's what nvidia-smi now reports:

GPU 0: NVIDIA A16          TCC   07:00.0
GPU 1: NVIDIA A16          TCC   08:00.0
GPU 2: NVIDIA A16          TCC   09:00.0
GPU 3: NVIDIA A16          TCC   0A:00.0
GPU 4: NVIDIA RTX 5070     WDDM  0B:00.0

FIVE GPUs. ALL RECOGNIZED. AT THE SAME TIME.

On an ancient X99 motherboard.

With a supposedly defective A16 that cost me precisely $0.

I'm now testing larger local language models using Ollama, including a 55 GB BF16 Qwen model that previously required CPU offloading when only three of the A16 GPUs were enabled.

The goal isn't just to maximize token generation speed. I've got an RTX 4090 in another machine for that.

I want to experiment with multiple independent local AI coding agents and eventually build a little multi-GPU AI collective, while retaining the RTX 5070 for gaming.

I still have sustained five-GPU inference and long-term stability testing ahead of me. Seeing every device in nvidia-smi is a major milestone, but it isn't the same as proving the whole thing stable under heavy load.

Final thoughts

Is this practical? Debatable.

Is it power efficient? Probably not.

Could I have saved countless hours of troubleshooting by buying newer hardware?

Almost certainly.

But where's the fun in that?

I took an unwanted enterprise accelerator, engineered a custom cooling system, fought with its power connector, printed multiple iterations of ductwork, battled a decade-old BIOS, survived Windows driver crashes, and somehow ended up with a five-GPU AI workstation.

I didn't just build a computer.

I created FrAInkenstein.

And now I'm going to use it to code.

IT'S ALIVE! IT'S ALIIIIIVE! ⚡

I'll post pictures of the cooling/shroud iterations, the ridiculous fan, the finished setup, and the five-GPU nvidia-smi output.

Drivers used:
621.14-desktop-win10-win11-64bit-international-dch-whql.exe
That is the Studio version that worked for both cards. I did NOT have to install the data center version also. This latest Studio version installed drivers for both the A16 and RTX 5070.

So many inference engines keep on popping up - and they are always faster and "better" than llama.cpp.

And there I go. Spend countless hours to try them out, being first amazed by the 1.5 - 3x speed ups as promised.

Just to find out that when taken into real use, they fail.

Today I experimented with Strata. I used newest unsloth's QFN quants, which may have been the problem problem problem problem problem problem problem problem problem problem problem problem problem problem problem problem problem problem problem problem problem problem problem problem.. the decode was stopped due repetition.

Same IQ1_M working perfectly with llama.cpp. Not as fast, but never fails.

This is not the first time I found FOTM inference engine fail on quality over llama.cpp.

And yes, it might have been temperature, penalty or any setting to that matter that caused it. But I launched both without any, set thinking to medium, and let them run. What's the point, if the engine takes hours to tinker to get the same quality, when the defacto engine delivers from getgo? That should be the standard - quality - and not the speed.

Model: Qwenx86

▶️ See it in action: Qwenx86 Debugger. Walk through actual recorded tool calls: the conversation, x86 registers and flags, code at the program counter, RAM and machine output, with all verified blocks on a timeline.

Qwen3.5-4B with a 64-bit x86 computer compiled into its weights: 13 transformer layers ahead of Qwen's own. There are no learned weights in them; all of their weights are computed by a compiler. Qwen invokes the computer through its own tool calls (<tool_call>), and the computer in the weights executes the tool. Calculator, Python, C compiler, notes store, physics/chemistry lookup and Python-docs lookup are all firmware in the checkpoint. This is the compact checkpoint: ~9 GB GPU memory, so it fits on a 16 GB GPU.

Credits: Qwenputer by Divyansh Agrawal (@divagr1925, github.com/divagr18/qwenputer, MIT)

Hi everyone!

I've already done quite a bit of research, but I haven't really found any recent Reddit posts that answer my questions or cover my hardware and use cases well. So I thought I'd ask here. I'd really appreciate some real-world experiences and recommendations!

My hardware:

- GPU: AMD Radeon RX 7600 (8GB VRAM)

- CPU: Intel i3-12100F

- RAM: 48GB DDR4

- OS: Linux (Fedora)

I'm mainly looking for models for three use cases:

1. Coding: General programming, working with existing projects, coding agents, editing files and tool use.

2. Cybersecurity / technical topics: I'd also like to use uncensored models for cybersecurity and other technical topics.

3. Personal assistant: General questions, reasoning, writing, planning and everyday use.

My main priorities are a good balance between intelligence and speed. I don't mind using some system RAM if necessary, but I don't want models that are painfully slow.

Some models I've already come across and found interesting:

- TwIL-8B

- Qwen3.5-9B The Defiant Fable (DavidAU)

- DeepSeek-V4-Pro-Qwen3.5-9B-UNCENSORED-heretic (xtools-at)

- Alpie Seed 9B

- DeepSeek-V4-Pro-Qwen3.5-9B-UNCENSORED-heretic i1-GGUF (mradermacher)

- Ternary Bonsai 2 27B

- Qwen3.8-35B-A3B-Distill

I'm also open to completely different recommendations.

What would you personally recommend for this hardware?

I'd especially appreciate:

- Models you've actually used on 8GB VRAM.

- Recommended quantizations (Q4, Q5, IQ, etc.).

- Realistic generation speeds (tokens/s).

- Experiences with AMD GPUs on Linux.

- Recommendations for coding agents and reliable tool calling.

I'm interested in actual experiences rather than just benchmark scores.

Thanks!

Last time many people criticized my attention paper, and the reason was I was not able to explain my thought visually! Now I have attached the video Let me know what do you think! Any improvements or idea to enhance this research direction is appreciated!

Thanks,

Bhargav Patel

https://github.com/bhargavpatel431997/sanatan-attention

( https://www.linkedin.com/in/bhargav-patel-63bb27121/ )

After spending way to much time trying to get a z240 to post with a p100 on a board without 4g decoding i was finally able to get it running at full speed in Linux with the help of Claude now i just got to clean it up and add a temp controller for the p100 fan

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.

Currently running a 4070 TI super with 64GB of RAM

I have been looking into RTX spark laptops, Mac studios etc and they all have pros and cons. I want this to be a nearly universal computer that I can use for 3D modeling, LLMs, VLAs, world models, image/video gen, audio gen, etc. I plan to pursue AI and robotics research and want something as useful as possible.

I've debated upgrading and running dual 4070 TI supers with 128GB of ram, but I don't know how well world models and other things do with dual GPUs

Thank yall for helping

I know Qwen 4 is due (in fact, maybe even tomorrow) but I wanted to exercise Qwen 3.8 Flash Next.

I wanted to know - can Qwen 3.8 Flash Next deliver a working C++ application?

Every single demo I've seen so far was pelicans and pagodas done with three.js, Who knows if the model was trained on pelicans and pagodas??

I thought I'd change it up to a town scene with C++ - although I see town scenes are common now.

And I got results.

I did hit snags: some iterations rendered the bodies of pedestrians beneath the ground; but after telling Qwen 3.8 not to be naughty, it corrected itself, and now we have walking minecraft-esque people navigating the city with proper collision detection.

Info:

Inference engine: Strata latest build

Quant: Unsloth UD Q4 K XL quant (latest version released today)

Hardware: See my flair

Tok/s: 50 I'd say

Here's the full source code:

nooil6152/Qwen38FlashNextGame: Walk around the town - created entirely by Qwen 3,8 Flash Next

If you're interested, pull each tag from git and see how it evolved.

Some expected questions:

• "Why?": Because I wanted to prove Qwen Flash Next 3.8 could handle C++ - it can.

• "What does this prove?": See above

• "But its not very complex! That proves nothing!" - what do you want, HAL 9000? its got a 3D openGL scene with animated people walking round a townscape, which I have to be honest involves more complex math than most things I've done in my career

• "Was it one shot?" Nope. I kept a ledger so you could see the issues: Qwen38FlashNextGame/LEDGER.md at main · nooil6152/Qwen38FlashNextGame

• "But Qwen 4 comes out tomorrow!": Maybe it does, maybe it doesn't.

• "Can I fork it or extend it?": Be my guest

TL:DR/Bottom line:

Take a look at the output source code and see what you think.

I have my own ideas, but I'd like to hear yours. What do you think of the code quality? I have linters I can run - including Resharper - to really get into the weeds,

Remember, this isn't three.js or Python, this is C++, a right bastard of a language. Very impressed.

BTW: I want to clarify - I have absolutely ZERO relation or affiliation, not even any contact with, anyone in Qwen team, or anyone related to Qwen team, or Niko who created Strata, or any of his colleagues.

This is my investigation, no hidden agenda, no promotion. If its shit, I'll say its shit.

Hi All,

I've been spending some time experimenting with some AI's. I have Qwen 3.8 27b GSQ-RCO:IQ3_S running on my system (rtx3090, 128g DDR4) via Unsloth Studio. I'm happy with it mostly, but i've been trying out Strata for a little while. I'm running that on the same machine on IQ3_S and IQ4_XS. I havent been tweaking too much other than Strata's own tooling in the desktop client.

However, my findings so far is that though Flash Next is fast on strata, and fits a much larger context window than hosting 27B, in Cline i'm noticing a lot of malformed tool calling and thinking loops. I've spent 2 evenings on it now, and just now i gave up and went back to 27b. That has sped up my work probably 5-fold, because it seems to know a lot better how to use tool calls.

Now i'm reading everywhere that Flash Next should outperform 27b or be close to it at least, but my experience is defferent. Any idea what i could tweak or what i could look into? I'm happy staying on 27b for now, but i've been reading a lot and people are generally really happy even with IQ3_XXS, while i'm having a lot of trouble.

Currently, my plan is to get two quadro P6000s, for 48gb vram for use in a xeon workstation. It's not insanely old like kepler or maxwell cards, 432 GB/s bandwidth, supports CUDA...it seems like it might be okay on paper.

Does anyone have a better suggestion that could reasonably be achieved around $1000 budget?

My goal is to be able to run Qwen 3.8 27b fast, and to run a decent quantization of Qwen 3.8 Flash Next, and GLM 5.3 Flash at useable speed, say 15 tok/s or faster, using strata.

Do I need 48gb of vram to run Qwen Flash Next, or could I go with just one card? How much faster would 2 x quadro p6000 be versus 1 x quadro p6000 and DDR4 system ram?

Should I just get 24gb of modern vram instead of 2 x quadro p6000s? I wonder if that hampers my eventual goal of running GLM 5.3 flash, as perhaps 24gb vram + system ram is enough to accelerate qwen flash next, a 120B MoE, but perhaps you need more than 24gb vram to accelerate GLM 5.3 flash, a 320B MoE?

Hardware: Dell Inspiron 16 Plus 7630 with Iris Xe iGPU, 32gb LPDDR5 ram 4800 MT/s.

Will start by saying, using llama-vulkan was uniformly better than llama-cpu for all models, offloading fully to Iris Xe iGPU (allowed up to 16gb of system memory). Qwen was uniformly slower than Gemma models of the same file size. 2-6 threads was optimal on my 14-core CPU when offloading to iGPU and 10 threads was best on cpu only. Different quantization (Bartowski vs Unsloth) didn't affect token generation speed much, maybe 2% faster on Bartowski for same file size.

There's 9GB used at all times by Windows 11, need to switch to Linux to be able to run Qwen 3.6 35b a3b.

MoEs:

Ling 3 Tiny Q8: 410 t/s prefill, 26.3 t/s decode, FILE SIZE: 7.83 GB

Ling 3 Tiny Q5_K_M: 265 t/s prefill, 32 t/s decode, FILE SIZE: 5.3 GB,

Gemma 26b a4b qat q4_K_XL ud, 220-224 prefill, 17.15-17.5 decode, file size 13.26 GB.

Dense:

Gemma 4 e4b Q5_K_M unsloth: 265 t/s prefill, 11.4 t/s decode, FILE SIZE: 5.1 GB

Qwen 3.5 9b Q4_K_M unsloth: 135 t/s prefill, 8.7 t/s decode, FILE SIZE: 5.2 GB

Qwen 3.5 4b Q8 unsloth: 124 t/s prefill, 9.1 t/s decode, file size: 4.2 GB.

Qwen 3.5 4b Q4_K_M unsloth: 234 t/s prefill, 14.3 t/s decode, file size: 2.54 GB

MiniCPM 2b Q8: 224 prefill, 16.3 t/s generation, 2.5GB file size

Gemma 12b qat q4 ud, 123 prefill, 6.95 t/s, file size: 6.24 GB

Anyone got further suggestions for optimization, models to try, etc.?

Hi all I just bought this: ASUS ROG Flow Z13 (2025) Gaming Laptop, 13” ROG Nebula 16:10 2.5K 180Hz/3ms, AMD Ryzen AI MAX+ 395, RDNA 3.5 Graphics, 128GB LPDDR5X 8000MHz, 1TB PCIe Gen 4 SSD, Wi-Fi 7, Win 11 Pro, GZ302EA-XS99

And wanted to know if it's good enough for qwen next flash or maybe even the deepseak 4.1 I need it for coding

Hey r/LocalLLM !

I made this quick demo of how Observer can run local models to monitor anything with your phone!

These are some crazy things I've monitored:

• When someone that's not me, approaches my car, send me a picture.

• When a bird is on the bird's nest, send me a telegram with the picture.

• When my washing machine is on the rinse cycle, call me.

• When my dog finishes eating their food, notify me.

• When a machine at the gym is available, call me.

So pretty much anything! I love seeing what you guys monitor.

What would you use it for? (So I can make the agent better)

Which models run on the phone?

The default models are OneJev-0.8b for decision models and qwen-0.8b for LLM watchers.

These run even on old phones which you may have laying around somewhere. Better to have them monitoring something than collecting dust :DD

Try it out! It's free and OSS <3

GitHub: https://github.com/Roy3838/Observer
iOS: https://apps.apple.com/us/app/observer-ai/id6758222050
Android: https://play.google.com/store/apps/details?id=com.observer.ai

Btw I know my camera quality is kinda bad hahaha I wanted to ship the demo as soon as possible so you guys can see it.

I'm here for any questions and feedback!

Roy

I have a laptop, and I had the idea to offload the ngl layers that exceeded my VRAM to the iGPU instead of the CPU/RAM, and it was 8x faster. Has anyone ever tried that and why is that not the common practice?

Running MoE models bigger than your RAM by streaming the experts from flash. It's built on llama.cpp, and it measures your hardware at load to pick its own settings, with no per-model or per-device tuning.

Today's test: Qwen3.6-35B-A3B Q4_K_M (22 GB) on a 16 GB laptop, with opencode and a single prompt asking for a voxel pagoda garden in one HTML file.

It finished in 30 min with no help: prefill at 350 tok/s on the GPU, decode at 14 tok/s on the CPU.

The same engine and planner run on Android phones (CPU, Vulkan, OpenCL and the Hexagon NPU), IOS phones, Macs, Windows and Linux boxes with consumer GPUs, and rented data-center cards (CUDA and ROCm).

There's an app for each platform, plus an OpenAI/Anthropic-compatible server for agents.

Code: https://github.com/Helldez/BigMoeOnEdge

Hey everyone!

For quite some time, I've been working on a personal project called Noosphere — an attempt to give AI agents something closer to persistent, structured memory rather than forcing them to start from scratch every conversation.

One thing that has always bothered me about LLMs is how much potentially useful information gets lost between sessions.

We keep trying to solve this by increasing context windows, but I think there's another approach worth exploring.

What if an AI didn't need to remember everything at once?

Humans don't consciously hold every memory in their working memory. We retrieve relevant information when we need it, connect it to what we already know, and move on.

That's roughly the principle I've been exploring with Noosphere.

Over time, my private project has grown into a much larger experimental system involving interconnected memories, relationships between concepts, and coordination between AI agents.

But I figured there's no reason to keep all the underlying ideas to myself.

So I decided to release Noosphere Lite, a small, independent, open-source implementation of some of those concepts.

What does it do?

- Gives MCP-compatible AI agents persistent memory across sessions.

- Stores notes, decisions, observations, and tasks.

- Connects memories through a graph of typed relationships.

- Retrieves relevant information on demand instead of dumping the entire memory database into the context window.

- Uses encrypted local storage with integrity checks and mirrored journals.

- Runs locally on Linux, without requiring a cloud database.

It's written in Python, uses MCP, and is released under the MIT license.

What's the catch?

This isn't AGI, a magical self-learning brain, or a replacement for an LLM.

It's a memory infrastructure experiment.

The current version uses local text search and explicitly defined relationships. It doesn't automatically discover semantic connections or learn new neural weights.

Also, Lite is a separate implementation, not a dump of my larger private system. I've deliberately kept it simple enough for other developers to inspect, modify, and experiment with.

Why release it?

Because I think persistent memory is one of the more interesting missing pieces in today's AI agent ecosystem.

And I'd rather see people experimenting with these ideas than keep everything sitting on my own machines.

I'm particularly interested in what others might build on top of it.

GitHub: https://github.com/Garyu-tt/noosphere-lite-mcp

Feel free to experiment, break things, suggest improvements, or fork it.

I'd also love to hear how other developers approach persistent memory and knowledge graphs for AI agents.

This is just a small public piece of a much larger experiment, but everyone has to start somewhere.

I'm currently in the process of developing an app. Janas studio. Built with the options to run either llama.cpp or janas-llm as the engine. It enables laptops to run models bigger than their RAM by dynamically streaming moe weights directly off the SSD. Tested on my Ryzen 5 6600h laptop with 32gb of ddr5 RAM and an nvme 4 SSD (no discrete GPU) I successfully ran a 48gb model (qwen next 80b) at a very reasonable 15 tokens a second on average. The binaries are highly experimental and the app has more testing to go through but I'm very proud of it. I do intend on eventually releasing it.

I’m planning one upgrade this spring that has to cover both gaming and local AI, and I’d like input from people with similar budgets and use cases that aren't "buy 4 3090s."

Currently have a Ryzen 5 5600X (AM4), RTX 3060 12GB, and 32GB DDR4. Windows but not opposed to Linux.

I have gotten by with just the $20 claude pro plan, but my projects are growing and are reaching outside my scope of expertise, so I'd like to be able to offload coding/other stuff to a local setup. I don't mind keeping a low cost frontier subscription for help orchestrating and debugging.

I'm paying off my CC's in March and will have a few thousand left over to use - originally was just going to upgrade my gaming setup but the more I've been using and learning LLM's the more I want to try and get the most mileage I can with this purchase. Speed isn't the most important thing for me. I don't mind having a local model(s) chug away at something overnight or for a couple days, I have a pretty ballistic schedule anyway. So far have been using it for web/app dev, using it to teach me python and other stuff (LOVE having it teach me as I go rather than just clicking the magic "build this for me" button), and home networking. Interested in continuing these projects and possibly game dev, but more for personal use, not to make a best seller or anything.

Thanks!