llms gpu-inference kog-ai developer-tools

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

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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.

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Coverage & discussions

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Analysis ready

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.