SK Hynix just dropped their latest earnings, and one detail stands out. All of their HBM and DRAM production is completely booked until 2029. Any new orders placed right now will only be delivered in 2030. This basically means zero price drops for RAM and everyday consumer devices until 2030. Hardware is as cheap today as it is going to get.

我可没那么说 不要造谣

Kapı kilitlerinde meşhur olan 1071 1453 gibi şifreleri deneyince 430 tane daha kritik altyapıya ait panel buldum. İlgimi çeken bir kaç tanesini buraya ekliyorum.

🔥 中午刚说 40 系还没支持，今晚 4090 到 4060Ti 全解锁了，连 FP8 都翻倍 40 系不能直接套 30 系的模块，是因为它换了另一套签名引导固件，多锁了四道权限门。今天研究者 TOTORONG 放出 AD10x 载荷，在驱动绑定前一次捅开四道门，而整个 40 系共用同一份引导固件，所以从 4090 到 4060 Ti 一下全通了——同样不刷 vBIOS、不改驱动。 1️⃣ RTX 4090：FP16/BF16 指令吞吐从 165.6 涨到 331.6 TFLOPS；500W 下跑 cuBLAS 8192³ 矩阵乘，TF32 从 77.5 涨到 165.9 TFLOPS（2.14 倍），3 张非公卡已复现 2️⃣ RTX 4080 SUPER：PyTorch 矩阵乘 FP16 从 110.6 涨到 202.7 TFLOPS（1.83 倍）；mmapeak 实测连 40 系独有的 FP8（FP32 累加）都从 201.8 涨到 376.1 TFLOPS 3️⃣ RTX 4070 Ti SUPER：240W 下 FP16/BF16 从 95 涨到 189.9 TFLOPS；跑 CUTLASS 4.8.1 原生 FP8 快累加矩阵乘从 158.5 涨到 309.1 TFLOPS（1.95 倍），322 万个输出单元零比特差异 4️⃣ RTX 4060 Ti 16GB 与 Win11：三卡同机 A/B 实测，182W 功耗墙下 BF16/FP16 仍涨 38–39%；Windows 11 也出了免核显、单卡直接亮机的 UEFI 引导版 💡 Linux 端装下方仓库（4080S 看原仓 PR #24）。40 系别用 targets=all，先跑 python3 tools/print_boot_option.py 拿到单卡参数加进内核启动项，关掉 Secure Boot，关机拔电源等几秒「冷启动」才生效；Windows 端去 Hugging Face 搜 TOTORONG/AD103_win_unlocker。 github.com/JustAResearcher/g… 中午还在等 40 系的各位，今晚敢拔电源试一把吗？

My 3090s just got 1.9x FASTER at BF16 tensor math!!! Same cards, same driver, same 300W, one Linux kernel module. bf16 tensor pipe: 82 -> 155 TFLOPS cuBLAS BF16 GEMM 8192: 64 -> 99 TFLOPS TF32: 41 -> 79 TFLOPS NVIDIA ships every 3090 with fp32-accumulate tensor math at half speed. The driver even reports it: FMLA32 = 1/2. gpu-unlock by Zagitta sets it to full before the NVIDIA driver loads. After a cold boot all 4 of my cards say FMLA32 = full. Real inference got less than I predicted. Strata, Qwen3.8 Flash-Next on 2 cards, prefill: 4K: +12% 10K: +12% 90K: 3205 -> 3536 tok/s, +10% Before installing I said +14 to 19% at 90K, so I was wrong. The GEMM phases got 23% shorter, but they are only about 40% of Strata prefill, and the int8 experts, copies and CUDA core kernels didnt get any faster. Outputs did not change. With the same expert cache size, my log-prob dump over 1996 teacher-forced positions is the same file byte for byte as the stock run before the module. Next is Strata itself, now its everything around the GEMMs that makes prefill slow. How I set it up is in the replies.