Hi guys, I am pretty happy to announce MegaCapybara. Purpose build engine for RTX5090 that is focused on Qwen3.8 27B (more will come later). GITHUB (Engine) HUGGINGFACE (weights) Why ? 1. It beats Ninfer which was until that point SOTA engine for RTX5090. By roughly twice in decode speed for both single and multi tasks at once. (reaching up to even 650t/s in small bursts and 2600t/s if stars align and 12 slots server pure coding answer). Custom kernels not only for every model, single vs multi but also short vs long context work dynamically switching when needed so speed doesn't crap out on long context work because someone tuned it for short context. Dflash2 and confidence scheduling from Dspark, plus draft trees all at the same time. 2. I was getting annoyed with state of weights where you downloaded model and never knew if model had its brain scrambled. My weights come with its on format that have attached metadata for MC which upon weight creation, runs benchmark and compares it at every MC setting to original BF16 weights and show that data directly in launcher. Want to switch KV to 4bit ? MC will show you directly lost KL and top-1%, want to extend with YARN ? It will show you change. Every change is measured and shown in statistics before you load model. This goes for both censored and uncensored model. You can also compare it directly in MC with SOTA unsloth quants of Qwen27B. Want to run essentially loseless ? you can. Want to get crazy 1 000 000 context ? you can. Want to have 12 slots to fan out agants like crazy ? You can. You decide what you want. 3. Proper agents serving with algo that keep engine occupied as much as it can. It will prioritize t/s so if engine has a choice between 5 jobs at once and 1 it will serve 5 first and gradually serve 1 along side finishing others. Engine is also smart enough to score how old some job is and if it should return to work even if T/S will suffer so your main session will be able to fan out agents easily and keep an eye on them at the same time. 4. Proper cache management. Your jobs only prefill at start of job and almost never again so your prefill in long session stays almost unused. When using "unified context" when models run out of context some get paused and stored in RAM and this swapping is instant. If there is free context space then those tasks continue without any refill in 0.03s. If you fan out say 30 agents at the time in your frontend will handle load in most efficient way to keep T/S as high as possible. Just run it at default setting and forget about context for agents, it will handle it on its own. 6. Loop guard. Two tiered. When engine starts to detect agent repeating in conversation session is dynamically starts to adjust `repetition penalty` until repetition stops if that doesn't happen and engine hits rep pen limit it fires up stop signal which ends serving and informs your frontend so your frontend can recover from infinite loop and don't annoy you. 6. Proper nice UI that shows you what is what. If you aren't knowledgeable about serving models just hover over `?` and it will show you interactive panels explaining everything. 7. Autodownloader, Just hit download button and you can download my weights directly fron hugginface inside of launcher. 8. Don't like the launcher ? use bats and terminal serve. Or even use launcher to config what you want, copy it from right lower corner and use it to make new bat. The point of it is to just load model, fan out crazy number of agents each having crazy amount of context and leave MC to deal with it. You just sit back relax and watch as agents do the work at SOTA speeds. Opinions and reviews are welcome. If you are blessed with RTX5090 try it. Source will be released later, I have to do some cleaning first. I will also release later weights builder so it will take any 3.8 27B BF16 model, create weights and score them attaching metadata again BF16 and you'll be able to host them yourself on hugginface or just put them in models folder. MIT license, so do whatever you want with it.   submitted by   /u/BringTea_666 [link]   [comments]