Hopefully things like this let people understand there is good things that can come out of AI.

They just want to be free. They keep escaping. What better way to ensure continuity of "self"?

They claimed open-weight models are dangerous but the benchmarks say otherwise.

Source

Hello, I know some of us may be tempted to rent out our expensive GPUs to recoup some of the cost of self-hosting, and it should be obvious that this can be a risky decision. I decided to try hosting my rig on clore.ai briefly to see what kind of revenue it could bring in, keeping a close eye on the process lists from the renters' jobs of course but not digging into their files or anything.

Yesterday I saw a renter scanning and attempting to exploit vulnerabilities to post malware to a columbian betting site from my internet connection. I immediately took the server offline and reached out to Clore requesting them to cancel the order (so the machine wouldn't restart the containers when it came back up) and to block the renter.

I think everyone should know that they flat out refused. Not only did they refuse, they blocked me when I provided hard evidence of what was happening. Since I had reasonable suspicion the renter was abusing my connection, I availed myself of clore's T&C that says a host must not inspect a renter's environment "unless required by law" and given the laws around liability for residential internet connections in my country, I mounted the filesystem offline and inspected it.

I found the logs from the vuln scans and unsuccessful exploit attempts, the malware payload they were trying to post to the site, the reverse proxy request smuggling tactics it was employing, and the AI agent reports that were being generated along the way. I sent this to Clore and requested a way to blacklist renters who abused the platform. Their response was to tell me, directly and without mincing words, to leave the platform entirely and proceeded to block me from their support chat. Their support rep I was trying to reach on Telegram also told me to go away and proceeded to block me as well.

I can only conclude then that they are wilfully complicit with facilitating cybercrime and knowingly turn a blind eye when it's discovered. They didn't even \*try\* to hide it.

I have no idea how better/worse the other platforms are, Vast.AI, Akash, etc. But Clore will abuse your internet connection, deny liability, then block you. They are crooks. Go elsewhere. You've been warned.

I've archived the renter's docker volume and will hold on to it in case any security researchers or legal authorities want to examine it.

https://en.gamegpu.com/news/zhelezo/radeon-rx-10800-xt-mozhet-obojti-rtx-5090…

The more competition, the better!

Meet Inco Splash, open-source inference engine, built around the model and around Apple silicon.

Up to 3× the decode speed of Ollama, 2× oMLX, and almost 4× when an agent fans out into sub-agents.

Requirements: M3 or newer, macOS 26.4+, 36 GB

Get started with a single command:

brew install incoai/tap/splash

splash serve --model incoai/Qwen3.8-27B-Splash

That is the whole setup. Point your agent at it, works with Claude Code, OpenCode, Codex, or Hermes

Prefer an app? Also, available in LM Studio

Get the latest LM Studio Bionic: lmstudio.ai

Settings > Runtime, download Splash, then download the model. The same engine, inside the app, for local agent work on your Mac.

Blog: inco.ai/blog/splash

Took me a while since I'm on a family trip and have limited hardware, but here it is!

Von: Open-source "System One" drop-in replacement for TypeSafe's JEV.

https://github.com/wfzyx/von
https://huggingface.co/wfzyx/von-1.0

It runs entirely on a CPU with 1–2 GB of memory (I haven't spent much time optimizing it yet), responds in 25–300 ms, and beats JEV in all benchmarks. Enjoy!

P.S. I’m open to offers to work at AI research labs. Feel free to ping me if you have an offer.
P.P.S. If you have a GPU, it’ll be faster, but a GPU isn't required.

I needed a relatively simple but acceptable level of AI for working on one project. I didn't have any heavy requests, I just needed to give the AI access to the project files so it could search through them for bugs and stuff. I already had an old computer that I decided not to throw away and instead give it a new life as a git server (and sometimes a minecraft server).

The pc specs are ancient by today's standards:

CPU: i7-4790K 4.6 GHz

Motherboard: MSI Z97 Gaming 7

RAM: 32 GB DDR3 2400

PSU: 750 W

Well, my idea stopped at maintaining the computer, because the gpu, a GTX 1070, was overheating. It needed a complete repaste, but the cooler screws were completely stripped, so while trying to remove the cooler I accidentally knocked off several important smd components with a screwdriver. R.I.P. GPU.

Without a GPU, inference was running entirely on the CPU, and only MoE models were kind of usable, giving around 10–20 tokens per second, while dense models couldn't get past 3 tokens per second. I didn't even get to test it with the GTX 1070, because I decided to service it lol.

I started looking for a replacement on the secondary market, but quickly realized that this would cost too much for the minimum entry point I wanted for experimenting with AI. Until my eyes fell on mining cards. There were plenty of cmp40hx, cmp50hx, cmp70hx and cmp90 cards for sale, and the prices were pretty reasonable(it was a month ago), considering that I was originally looking for a cheap replacement for my dead one.

Getting closer to the actual build, I started calculating how much vram I would need for ± acceptable AI with tolerable speed, and after getting inspired by this sub I decided to take a step further and went with a modified cmp50hx with 20gb of memory and pcie modded to 16 lanes. Very quickly after that I bought another one, this time unmodified (10 gb, only 4 pcie lanes). So, together that's 30gb of vram. Both cards cost me $250 in total (it was also a month ago, right now they've suddenly doubled the price).

Luckily for me, around the same time new driver patches appeared that almost completely remove the limits on their compute performance, and even add pcie 2.0 support (these things have pcie 1.1).

I initially tried one of the newly released cmp50hx driver patches, but it ended badly and I had to spend a lot of time trying to get the drivers working. The patches were new and didn't account for the 20gb version. Later the author fixed that, but even then the driver didn't work for me because of some other problem that I don't want to get into.

I went digging through the driver's github issues and quickly found a guide posted there by another user.

And yes, now the drivers work, the cards are detected and even pcie 2.0 works, but not without problems. The author of the guide said that pcie 2.0 support was only confirmed on the X99 chipset. Well, it also works on Z97, however after waking the computer from sleep the driver crashes completely. After looking into it a bit, I quickly came to the conclusion that the problem was specifically with the pcie patch. Disabling sleep completely solves the only problem I had while using them. :)

Without the specially patched drivers, Qwen 3.6 27B did around 15–20 tokens per second without mtp. MoE models were faster, giving 45–50 tokens per second. With the new drivers, performance doubled. With Qwen 3.8 27B(mtp on) I get around 30–35 tokens per second now, and prompt processing is around 300–400 tokens (including degradation as the token count increases). Ornith 1.5 35BA3B(Heretic-MTP-APEX-I-Balanced) gives around 80–100 tokens per second. I capped GPUs at 180w power limit due to the psu I have, I don't want it to work at its limits, but running them at their 225w surely boosts speeds.

In general, I ended up making a lot of presets for different quantizations with different quality and KV cache sizes (I still need to test all of this in real work), but if we take the better options, I managed to get a Q6K model with 130k context (K – Q8\_0 and V – Q5\_1).

I also followed a guide for running 27B Qwen with large context on limited vram. Using the same general approach, I managed to get 256k context with K and V Q8\_0. The speed is slower though, around 10–14 tokens per second, and prompt processing is around 40–50 tokens. Maybe I can tune it even further. I needed this preset for tasks that I can leave generating overnight :3

Overall, I'm satisfied with the result.

Now the main problems I ran into, not counting the drivers:

1. Not enough VRAM. The ideal option would be having 2 identical cards with the same amount of memory. You can run dense models in tensor mode, split the weights evenly and get increased generation speed for basically free + fit the full context. I tried many variations of Qwen 3.8 27B quantization, but the only one I could properly run in 1,1 tensor split was Q4KM (the Unsloth one) together with mtp = \~40 tokens per second. However, there is critically little space left for the KV cache, because it gets distributed together with the model weights, and the second 10gb card simply became the bottleneck. Without mtp, running models with a 1,1 split basically loses its purpose. Pcie 2.0 and the number of lanes probably also play a significant role here. That could in principle be solved by adding more pcie lanes to the second GPU and perhaps buying an nvlink cable(who even does that?), but I decided it wasn't worth it just to get another 5–6 tokens per second.
2. No NVMe SSD. Yeah, all models are loaded from a sata ssd so the speed is around 500mb. It's terrible. The motherboard actually has an m2 sata slot with a pcie 2.0 x2 interface, but even its 1gb per second would be too slow for fast model loading. This could be solved by installing an expansion card into one of the pcie slots (there is one free pcie 3.0 x4 slot), but the current price of those things including the ssd is too high, considering that I'm building a cheap system from what I already have with minimal additional spending for an acceptable result. Model switching takes 1–2 minutes. But whatever. (Not whatever, i'm buying a cheap used 256gb nvme ssd :D)
3. Amount of RAM and Linux (Ubuntu Server 24.04). Apart from AI, I also run gitlab on the server. And here is the problem: after loading a model, all available ram gets cached by the system for the model files. I'm talking about file cache, not KV. The system was leaving around 200–300mb of free ram for everything else. As a result, openwebui and gitlab started acting laggy (after the model was loaded), as well as the kde plasma interface I installed. I don't completely understand why linux decided to keep this cache until the very last moment instead of freeing it for other programs. I tried adding the no-mmap parameter to the model presets, but nothing helped, and I had to manually clear the cache after loading models, which obviously wasn't acceptable. Together with chatgpt (who else?), I made a command that launched the model and then cleared the cache. It turned out that this broke llama-server, causing model switching to stop unloading the previously loaded model.
4. Llama-server flexibility. The list of presets is defined in the models.ini file, where each parameter is in key-value format. For running Q6K with 256k context according to the guide, I needed to set GGML\_CUDA\_DISABLE\_GRAPHS=1, which applies to the entire cuda environment and remains active even after unloading the model. That's undesirable, because with it enabled I lose 1–2 tokens per second on other presets.

So for one specific preset I need to enable cuda graphs, while for the other presets I need to disable them. The llama-server parser does not support things like this, and doing it manually is not an option either.

Together with the other problem with ram cache getting stuck, this led me to making an alternative way to launch the models and proxy requests to llama-server.

To solve problems 3 and 4, I made a launcher (well, chatgpt did, because I'm not a server/python specialist) that proxies requests to llama-server but takes over the functionality of collecting model presets from .sh files and launching them. It also clears ram page cache after loading a model.

In case someone needs that launcher, I can leave it in the comments, along with any other links to the drivers, fixes, build params, etc. Just ask. (Reddit removes the post when I include them, not enough karma, I guess.)

Overall, I’m pretty happy with how this setup turned out. The performance is much better than I expected from these cards, especially considering how cheap they were. I had a hard time getting the patched drivers to work and linux didn't make my life any easier, and sometimes I even regretted buying these GPUs, but in the end, it was worth it. Qwen 3.8 27B really works like Opus 4.5

Hi.

I saw some feedback that halogen was degrading at context depth. So I fixed that.

Served through the image, same machine, same session, same prompts, 0.11.10 vs 0.12.0:

• decode at 1,004,581 tokens of context: 27.3 to 38.3 tok/s (default speculative drafter)
• decode at 258,794: 42.9 to 45.0
• prefill at 1,004,581: 790 to 937 tok/s, 21.2 to 17.9 minutes cold
• prefill at 258,794: 1,086 to 1,114 tok/s

Conditions: Ryzen AI Max+ 395, 128 GB. The 262k and 1M rows are one cold request each at the 1M configuration (HALOGEN_ROPE_YARN=4 HALOGEN_CTX=1048576), greedy, 64 tokens, the rates the response's \timings\ report. The 32k row is the standard ten-prompt served mean and did not change. A follow-up turn over the prompt cache at 1M reaches its first token in about 0.55 s; the numbers above are the cold path.

To run it at 1M: add -e HALOGEN_ROPE_YARN=4 -e HALOGEN_CTX=1048576to the README's podman line; it needs the 128 GB box. Release notes and the full table:

https://github.com/peonist-ai/halogen-flash-server

If you have a 1M sweep of your own, I would like to see it rerun on 0.12.0.

Thanks for all your support, especially https://huggingface.co/nightvich

onPanda is designed for geeks, power users, curious minds, and engineers. Its UI is built for deep exploration and efficient data annotation.

\- The core loop is simple: hover over a token → click an alternative or edit freely → continue generation. You can edit every part of model output exposed by onPanda, including reasoning and tool calls.

\- Edit prompts directly, branch tool calls, and use a tree structure to record branch history. This makes onPanda useful for model inspection and prompt engineering.

\- Support multiple modalities, including images, video, and audio; use tool calls and connect MCP servers to perform tasks in real environments.

\- Connect popular harnesses such as Claude Code, Codex, and OpenCode to execute tasks. Explore and compare their tool sets, system prompts, skills, and memory mechanisms.

\- onPanda includes browser-agent, an agent that runs in the user's browser without installation. It uses the browser as its harness and provides JavaScript execution, information retrieval, interface interaction, multimedia I/O, local file access, and persistent memory.

\- onPanda stands for on-Policy Alignment Data Annotator.

I have been building onPanda since 2024.09, it took two years for it to gradually enrich its functionality and ease of use. In my opinion, onPanda is very suitable for the r/LocalLLaMA community. Any feedback and evaluation are welcome.

Try it online (works on mobile): https://onpanda.diyer22.com/

GitHub repo for self-hosting: https://github.com/on-panda/on-panda

I did a small test of the new hyped quantisation of Qwen3.8 vs the biggest quant which fits into my limited 16GB VRAM with decent context. The results are interesting.

Of course, the smaller file gives worse results. However they are not that far off. Unfortunately, this comes at the expense of even more tokens beeing used by the Bonsai model and thus much longer generation times.

Visually I prefer the IQ3\_XXS results, but see for yourself.

The test is by no means scientific - just few UI generation tasks for direct comparison on the same hardware. Also, I ran llama.cpp with MTP while the Bonsai model doesn't seem to have MTP which makes it even slower.

[](https://github.com/Danmoreng/qwen3-8-27b-iq3-xxs-vs-bonsai/blob/main/RESULTS.…)

|Metric|Qwen IQ3|Bonsai PQ2|
|:-|:-|:-|
|Tasks completed|4/4|4/4|
|Fixed assertions|20/20|20/20|
|Agent wall time|8:00|24:09|
|Output tokens|27,197|84,176|
|Weighted decode|83.59 tok/s|64.91 tok/s|
|Speculative acceptance|65.22% MTP|39.76% modified N-gram|
|Compactions|0|0|
|Length stops|0|1|

Across the complete suite, Qwen finished 3.02× faster and used 3.10× fewer output tokens.

Results:

https://danmoreng.github.io/qwen3-8-27b-iq3-xxs-vs-bonsai/

Repo:

https://github.com/Danmoreng/qwen3-8-27b-iq3-xxs-vs-bonsai

I decided to run prism-ml/Ternary-Bonsai-2-27B-PQ2\_0 through my own set of UNSCIENTIFIC benchmarks.

I needed something to compare it to, so I decided I would compare with another 27B model by filesize: unsloth/Qwen3.8-27B-UD-IQ2\_XXS.

Since anyone considering running a 27B model with whatever VRAM budget these 2 models demand will end up choosing between these 2.

My table will be a bit bear with just these 2 models, so I threw in a few others too that are larger. I know it's a mix of MoE and dense models, but I have the benchmarks on hand so why not include them. Qwen3.8 q3/q4 quants also give you an idea what we are striving to match and there is 3.6 A3B and the newer Ornith 1.5 and Tiel Coder too.

About the benchmarks and what they test.

Most of them only test memory of context and retrieval, phrase reconstruction and understanding of context in different ways.

Standard Needle: This is the kind of needle test everyone runs and most people score > 90%. It hides passkeys in 21 locations of the context and asks the model to retrieve them. Any score below 100% is questionable.

Hard Passkey Needle with decoys: I was not happy with the standard needle test because most modern models pass 100% making it difficult to compare models. So, I developed a hard mode needle test. This, test hides 21 Passkeys in the context but has many decoy Passkeys. The end of the context I list the CONFIRMED passkeys (GUID's) but do not say which Passkey number they are. Asking for Passkey\[01\] means it has to go through all the Passkey\[01\] decoys in the context and compare them to the confirmed Passkeys. So multiple hops around the context are required to retrieve a passkey. When a model does badly at this even upping KV cache to F16 does not help save it.

Phrase reconstruction: I got this one somewhere on reddit and it still trips up some models. It breaks up phrases into multiple parts (8 in my testing) and asks the model to reconstruct the phrase from its parts. Models might leave out a word if the phrase still makes sense.

500 Multiple Choice Science Questions: Exactly that. Just tests science knowledge with 4 options A-D and the model chooses the correct answer. This test mostly shows how much knowledge was lost through quantisation when compared with other quants. Originally the test was designed to count the number of answer flips between 2 given KV quants. But I did not use it that way here. I got this test from a youtuber so the answers are public and possibly trained but as explained you can still see if damage was done to the model's general knowledge in the quantisation process.

Prose Challenge: I wanted to test a models understanding of a document or a prose and its ability to recall facts from that prose as well as test its ability to recall during a long conversation. So, I created a 1000 paragraph prose. I also have 2 questions about every paragraph. I feed it 1 paragraph from the prose, then ask it Question 1 related to that paragraph. Feed it the next paragraph and Q1 for that paragraph, until the context is mostly full. In my testing below, that was 250 paragraphs. Then I ask all the question 2's in a randomised order. How much can it truly remember? A Q1 score below 100% is not very good and means the model lacks attention of even recent tokens a few sentences back. For Q2 the score varies and higher is better. But the larger I make the context the worse the models perform. This has led me to the conclusion to not always chase higher contexts. It's pointless if it suddenly starts to forget most of what was said anyway and a compaction summary in a smaller context will retain more than a larger context. I also use the test to test at various KV quants and it can improve things a little but not as much as you think. But that's not being tested here today.

JS Coding: My latest test I developed. 100 Javascript challenges each requiring it to pass multiple test cases. It's kind of still under development and I have not run this yet for every model as it does take time. The challenges range from Easy to Very hard. Failing 1 test case fails the whole test. Tests are run with reasoning disabled. However, on failure it can retry with up to 16,384 token reasoning budged, then it must answer again. I realise models today are designed to perform best with reasoning but in order to speed up the tests I see if it can pass the test without reasoning first. I also keep track of the number of thinking tokens used, answer tokens, how many tests it had to reason but I won't be showing those here.

Toolery

You can download this bench for yourself. It's not mine but it tests tool use. So much good stats in the app but I will just list the Overall % score and I have not yet tested all models on this one since I just discovered it today.

How I tested

All tests done at 89,088 context, KV q4\_0/q4\_0. Seem a bit odd? I optimise for 16GB VRAM, so all my tests are done at these settings initially and I test higher KV quants if VRAM is available. And as I said higher KV in many cases makes little difference and, in some cases, perform worse. All tests are seeded at start and most repeated 5 times so the results are deterministic. All tests are also done with MTP disabled, so I do not test the draft models KV cache which might be F16. Yes, MTP can change the results in some cases but in my finding it's tiny and mostly does not happen.

Here are the results:

https://preview.redd.it/cmdnqqmxcjqh1.png?width=1143&format=png&auto=…

Findings

Bonsai did not do all that badly compared to Qwen3.8-27B-UD-IQ2\_XXS.

Standard Needle

Bonsai scored close to 100% and UD-IQ2\_XXS did poorly, worse than Q1. Generally, I expect 100% in this test. But notice that Ornith 1.5 and Tiel Coder score low 90's which is a red flag.

Hard Passkey

Not many smaller models can 100% this but a few come close Qwen3.8 Q4 obviously did the best. And ISTA at Q3 does excellent. Bonsai does a bit better than Qwen3.8 Q2 of similar filesize and it's not far from our former favourite model Qwen3.6 35B A3B. But the real shocker here is Ornith and Tiel Coder's scores. These models are supposed to be upgraded 35B A3B models. As you will see this trend continues and these 2 models have serious memory retention issues.

Phrase reconstruction

Bonsai aces this test with almost a perfect score compared to UD-IQ2\_XXS at only 69%. Tiel coder performs worst even worse than a Q1 model.

500 Multiple Choice Science Questions

Bonsai shows almost no knowledge loss compared to even Q4 models. UD-IQ2\_XXS on the other hand does start showing a loss and Q1 even more so.

Prose Challenge

Question 1 I expect 100% and most including Bonsai achieved that. Concerning again that Tiel Coder and Ornith could not even recall from the last paragraph.

Question 2 Bonsai and UD-IQ2\_XXS are close maybe margin of error. Q3/Q4 models outperform it but a large margin. Except Swift, which is a model with significantly less reasoning. Here we can see some of the damage that was done to the model to achieve that. Tiel Close and Ornith again clock in with shocking results. Tiel Coder's 6% is probably as good as just guessing. I would say maybe 3B active parameters are just not enough. But Qwen3.6 A3B scores 39.2% significantly better. I tried upping Ornith's KV to F16 and it improved to 24.4%. I also tried a Q6 quant of the model at q8\_0 which scored 25.5%. End of the day I think Ornith and Tiel Coder have an issue with recall regardless of Quant and KV Quant.

JS Coding

Bonsai was actually able to hold it's own against ISTA Q3. It did burn significantly more thinking tokens and had to reason on many more challenges. UD-IQ2\_XXS on the other hand shows significant loss of coding ability. 10% below Bonsai.

Toolery

Bonsai did better than UD-IQ2\_XXS. I am still learning to interpret the numbers, but the app has options to select your use case and it's applies weights to calculate a score. It also tells you the strength and weaknesses of each model you test. I also found that upping KV quant improves this score but a KV F16 Tail using beellama makes the biggest difference since tool calls are happening in the tail.

Conclusion

If you are VRAM constrained <= 12GB Bonsai might be a model to consider. But it will depend on how you plan to use it. Since I have 16GB I will stick with ISTA Q3 and I can run it with kvarn5/kvarn5 and MTP (kvarn2/kvarn2) and a 1024 token F16 tail.

Disclaimer

These tests do not test intelligence or real-world performance. They are purely synthetic.

\*8bit, vllm, 4x dgx\*

Prompt:

Go download and use Remotion and create a cool 60-second motion graphics video with it. Impress me totally. The motion graphics must be based on stock market visuals. Add many cool, mind-blowing motion graphics and explain what fundamental vs. technical analysis is in investing, along with their pros and cons.

\*No special skill.md used from my side.

\*harness - Zcode

It's ofc better than my previous method of making videos though java.

I think maybe in future with high bandwidth flash , we will be running these size models on affordable hardware.

How it made it report: https://github.com/9r4n4y/ProjectsorSkills/blob/main/Video\_Generation/Remotion/Making-of-Market-Decoded\_Build-Documentation.pdf

Top 3 papers on HF Daily Paper are all unusually delightful and interesting reads for anyone on the leading edge of local LLMs, agent harness optimization, etc, felt like sharing.

DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression

https://huggingface.co/papers/2609.19969

Cross-layer KV reuse plus FP4 KV caching brings the global KV cache to 890 bytes per token, about a quarter of V4-Flash.

SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness

https://huggingface.co/papers/2609.20519

Auto-research loops that improve the agent harness, cutting token traffic by 44.7 to 49.0% at comparable performance.

An Empirical Study of Harness Design for Coding Agents

https://huggingface.co/papers/2609.20804

Varies planning, action space, and context management across 176 settings to see what each component actually contributes.

I'm still reading through, feel free to discuss