KARINA ’16 Bit’+NINGNING ‘I Love You But I Gotta Let You Go’ Recording Behind #aespa# #æspa# #에스파# #KARINA# #카리나# #16Bit# #NINGNING# #닝닝# #ILoveYouButIGottaLetYouGo# #SYNK_COMPLaeXITY# #aespa_SYNK_COMPLaeXITY#

Built a Deepseek vs Qwen 16 Bit Game using Deepseek V4.1 Flash 1017 on EXL 3.5bpw Built in the Deepseek Harness and OAUTH GPT 2.5 Image to make the Sprites and then having Deepseek build out the game. Thinking to do another run using Qwen 2.1 Image Next.

48 years ago this week, Intel introduced a 16-bit microprocessor that would go on to transform personal computing for decades to come. We're proud to wish the 8086 processor the happiest of birthdays and want to thank Intel fans and users for keeping x86 alive all this time. Learn more about Intel 8086 and x86 here:

LTX-2.5 🤠 IC LoRA SDR To HDR Converts 8-bit standard-dynamic-range (SDR) video into 16-bit high-dynamic-range (HDR) video, using the SDR clip itself as the control signal and writing EXR output in addition to an HLG master.

16GiB just for the chat cache. For Llama 3.1 8B, that's the calculated size of a full 16-bit KV cache at 128K tokens. Weights and runtime go on top. When someone says a model fits on a GPU, ask what context they're using. What GPU and model are you trying to squeeze more out of? 👀

The Thunder stay perfect in the postseason and return to the Western Conference Finals ⛈️ See how SGA (35 PTS) and Ajay Mitchell (28 PTS) helped secure OKC's 2nd consecutive 4-0 series win in Chasing History presented by @MichelobULTRA.

I’m evaluating a batch of quantized Qwen3.8 27B models (non-GGUF) on 20K+ prompts across a variety of tasks: cyankiwi/Qwen3.8-27B-AWQ-INT4 — done unsloth/Qwen3.8-27B-NVFP4 — done EschaLabs/Qwen3.8-27B-Escha-W2 — done ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ — running Mia-AiLab/Qwen3.8-27B-EXL3-3.5bpw — pending (still figuring out whether it can support high concurrency; if not, it won't make it to the list) I can include one more model. Any recommendations (must be non-GGUF)? Maybe NVIDIA's NVFP4? I’m also evaluating all the models I made here: So far, everything looks good except for the smallest model, whose evaluation is still running. Some of my quantizations use 8-bit activations. While they don’t appear to hurt accuracy, I’m not seeing any significant speedup either (on Blackwell). I’ll run a few ablation experiments with 16-bit activations to get a better sense of the tradeoff. These are non-agentic evaluations. For agentic evals, I’ll focus on two models: the fastest 4-bit version and the lowest-bpw model that preserves accuracy. Once everything is done, I'll publish all the results, including latency and token efficiency.