Inference optimization and acceleration

We make a big model cheap and fast without losing answer quality.

• Quantization: GGUF (llama.cpp), AWQ/GPTQ (vLLM), INT8/FP8;
• Pruning and distillation into compact students;
• Speculative decoding, continuous batching, KV-cache optimization;
• Before/after benchmark under your workload: tokens/s, P95 latency, cost per 1M tokens;
• Target platforms: from RTX 4090 and ARM SBC (RK3588 NPU) to Kubernetes clusters.