2,813 karma · joined August 16, 2022
The most positive effect it has had on me is to make me enjoy even more deep conversations with my friends.
This is wrong. Git does store full copies.
Whoch likely mean doubling the required size of the battery pack, making it infeasible.
It’s not that much in terms of bodyweight on a regional jet compared to a long haul.
Any experienced programmer knows syntax is a detail, and would not be deterred by that.
> > We promise that we will not enforce the copyleft provisions in AGPL v3.0 against you if your application ... [set of conditions]
You need to like them to update the weights of the algo, it works well
> The 1.8-bit (UD-TQ1_0) quant will run on a single 24GB GPU if you offload all MoE layers to system RAM (or a fast SSD). With ~256GB RAM, expect ~10 tokens/s. The full Kimi K2.5 model is 630GB and typically requires at least 4× H200 GPUs. If the model fits, you will get >40 tokens/s when using a B200. To run the model in near full precision, you can use the 4-bit or 5-bit quants. You can use any higher just to be safe. For strong performance, aim for >240GB of unified memory (or combined RAM+VRAM) to reach 10+ tokens/s. If you’re below that, it'll work but speed will drop (llama.cpp can still run via mmap/disk offload) and may fall from ~10 tokens/s to <2 token/s. We recommend UD-Q2_K_XL (375GB) as a good size/quality balance. Best rule of thumb: RAM+VRAM ≈ the quant size; otherwise it’ll still work, just slower due to offloading.
Content-based chunking like Xethub uses really should become the default. It’s not like it’s new either, rsync is based on it.