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Real-time LLM Inference on Standard GPUs: 3k tokens/s per request

Real-time LLM Inference on Standard Datacenter GPUs (3,000 tokens/s per request)

blog.kog.ai

May 29, 2026

18 min read

🔥🔥🔥🔥🔥

58/100

Summary

Kog AI has launched a tech preview of the Kog Inference Engine (KIE), achieving 3,000 output tokens per second on 8× AMD MI300X GPUs and 2,100 on 8× NVIDIA H200 GPUs using FP16 without speculative decoding. The preview currently supports a 2B model, with plans to add support for large third-party MoE models at similar speeds.

Key Takeaways

  • Kog AI launched the Kog Inference Engine (KIE), achieving 3,000 output tokens per second on 8× AMD MI300X GPUs and 2,100 on 8× NVIDIA H200 GPUs.
  • The primary bottleneck for fast token generation on GPUs is memory bandwidth, which limits decoding speed during autoregressive decoding.
  • Optimizing single-request latency is crucial for AI agents, as it significantly impacts user experience and productivity in iterative workflows.
  • The KIE tech preview demonstrates that standard datacenter GPUs can achieve speeds comparable to dedicated inference hardware by optimizing the software stack.
Read original article

Community Sentiment

Mixed

Positives

  • Achieving 3k tokens per second on standard GPUs opens up exciting possibilities for real-time applications, making advanced AI more accessible.
  • The demo showcases the potential for rapid inference, hinting at future advancements in AI capabilities and user experiences.
  • The focus on standard GPUs rather than custom chips suggests a push towards democratizing access to powerful AI tools.

Concerns

  • The comparison of a 2B model against much larger frontier models raises concerns about the fairness and relevance of the benchmarks presented.
  • Some users feel that the term 'standard GPUs' is misleading, as it primarily refers to high-end data center GPUs rather than consumer-grade hardware.
  • The performance of the small model in the demo has been criticized for lacking depth, indicating limitations in its practical applications.

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