Kog AI Achieves Real-Time Large Language Model Inference at Three Thousand Tokens Per Second on Consumer Graphics Processing Units
Kog AI has demonstrated local inference speeds of three thousand tokens per second on consumer-grade hardware. This breakthrough relies on advanced speculative decoding and prefix caching. This drastically reduces local response latencies.
Why it matters
You can now run blazingly fast local code generation pipelines offline, matching or beating cloud API speeds without ongoing operational costs.
TL;DR
- 01Enable prefix caching on your local LLM engine to bypass processing static system prompts.
- 02Set up a small draft model alongside your main coding model to activate speculative decoding speeds.
- 03Ensure your combined active models fit entirely within VRAM to prevent performance-killing memory swaps.
Memory Bandwidth is King
At single-request decoding, models are limited by the speed at which weights move through the memory hierarchy. Kog AI optimizes MBU (Memory Bandwidth Utilization) to overcome this bottleneck.
Eliminating Overhead
Standard stacks suffer from excessive kernel launch overhead, which consumes valuable microsecond budgets. Kog systematically fuses kernels to keep the GPU streaming parameters without pauses.
Future Gains
New architectures arriving in late 2026 are expected to provide 4x higher memory bandwidth, potentially allowing similar performance for much larger models.