Alok on X: "Local AI optimization is officially outpacing hardware decay. I spent the last 3 hours building llama.cpp from scratch and benchmarking Google DeepMind’s new Gemma 4 26B A4B MoE on a prehistoric 8 year old, $500 NVIDIA Tesla T4 GPU. The results absolutely break the https://t.co/BEaC8jyCS

Local AI optimization is officially outpacing hardware decay. I spent the last 3 hours building llama.cpp from scratch and benchmarking Google DeepMind’s new Gemma 4 26B A4B MoE on a prehistoric 8 year old, $500 NVIDIA Tesla T4 GPU. The results absolutely break the conventional rules of inference. Here is the raw data running unsloth/gemma-4-26B-A4B-it-qat-GGUF via llama.cpp on a completely free Google Colab Linux (Ubuntu) instance: - 35k context: [ Prompt: 788.8 t/s | Gen: 47.4 t/s ] (-ngl 99) - 80k context: [prefill: 490.8 t/s | decode: 20.4 t/s ] - 180k context: [ Prompt: 242.4 t/s | Gen: 11.1 t/s ] - 250k context: [ Prompt: 220.2 t/s | Gen: 8.9 t/s ] llama.cpp flags: ./build/bin/llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -p "Explain the concept of open source software to a 10 year old." -n 24000 -ngl 99 -c 250000 Yes, I shoved a quarter million tokens of context into a 2018 data center card, and it generated at nearly 9 tokens/sec decode throughput without a single Out Of Memory (OOM) crash. For the hardware nerds, here is the exact environment from my nvidia-smi: - Driver Version: 580.82.07 - CUDA Version: 13.0 - GPU: Tesla T4 (Turing Architecture) - VRAM: 15360MiB (16GB GDDR6) - Power: Sipping just 16W at idle, capped at 70W TDP Google DeepMind really cooked with the Gemma 4 26B MoE (Mixture of Experts) architecture. But the real heroes here are the open source chads. Combining Unsloth's QAT (Quantization Aware Training) quant with the brutal C++ efficiency of llama.cpp allows us to push 50 tokens/sec on hardware that belongs in a museum. What does this mean for you? 16GB VRAM is the ultimate sweet spot for local AI enthusiasts right now. If you own a single RTX 4060 Ti 16GB, RTX 4070 Ti Super, RTX 4080, the new RTX 5070 Ti 16GB, an older 30 series like the RTX 3080 Ti Laptop card, or cloud GPUs like the A10G and L4 you are sitting on an AI goldmine. You already own the future. You don't need to rent cloud APIs. You just need to compile your tools correctly and let your VRAM do the heavy lifting. If you have a 16 GB VRAM GPU, run this and share your numbers for the community. Model's huggingface link in the comments.