Kog AI achieves 3,000 tokens/s LLM inference on standard GPUs
Original: Real-time LLM Inference on Standard GPUs: 3k tokens/s per request
Why This Matters
Breakthrough in GPU inference speed could enable real-time AI agent applications
Kog AI launched tech preview of Kog Inference Engine achieving 3,000 tokens/s per request on 8x AMD MI300X GPUs and 2,100 tokens/s on 8x NVIDIA H200. The system optimizes single-request decoding speed for AI agents through memory bandwidth maximization rather than FLOPS optimization.
Kog AI's preview demonstrates real-time LLM inference using a 2B model with plans for large MoE model support at similar speeds. The company argues that AI agent workloads require fast single-request decoding rather than aggregate throughput, as agentic workflows involve sequential operations where each step depends on the previous one. For workflows generating 50,000 tokens, their system reduces processing time from 8 minutes at 100 tokens/s to under 20 seconds at 3,000 tokens/s. The breakthrough comes from co-designing model architecture, runtime, and low-level GPU code as a latency-optimized pipeline, focusing on memory bandwidth maximization. The system runs on standard datacenter GPUs that enterprises already own, avoiding proprietary silicon lock-in. A live coding playground is available for testing at playground.kog.ai.