OS Megakernel that match M5 Max Tok/w at 2x the Throughput on RTX 3090
LLM speculative inference server for heterogeneous hardware & consumer GPUs. On an RTX 3090 power-limited to 220W: - 411 tok/s vs 229 tok/s on M5 Max (1.8x) - 1.87 tok/J, beating M5 Max efficiency - 1.55x faster decode than llama.cpp on the same GPU - 3.4x faster prefill The RTX 3090 launched in 2020.
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High-efficiency LLM inference engine in C++/CUDA. Run Llama 70B on RTX 3090.
LLMKube
github.comKubernetes for Local LLMs with GPU Acceleration
LLM Inference Calculator
llm-inference-calculator-delta.vercel.appEstimate VRAM, Latency, and Throughput
Run a 110B on a 2016 PC with 16 GB RAM. Know your tok/s before you download. Placement beats budget: predicts speed + memory fit for any GGUF on your exact hardware, self-calibrates, emits the exact llama.cpp command — or 'quantprobe auto' does it all. Falsification-tested laws; misses published at full size. pip install quantprobe
Can your GPU run this LLM?
github.comCalculate token/s & GPU memory requirement for any LLM. Supports llama.cpp/ggml/bnb/QLoRA quantization