Kog AI releases 3000 tok/s reasoning engine: the era of real-time reasoning for AI coding agents is here

Core conclusion

Kog AI today released a technical preview of its inference engine, Kog Inference Engine (KIE), which achieves generation speeds of 3,000 tokens/second per request on standard data center GPUs (8-card AMD MI300X nodes) – approximately 30 times faster than common APIs such as ChatGPT. The core breakthrough lies in its innovative Delayed Tensor Parallelism (DTP) architecture and single-core continuous decode kernel (monokernel), which solves the communication bottleneck of LLM inference from the architectural level.

Key Points

• Time of incident: 2026-05-29
• Core indicators: 2B parameter encoding model, batch size 1, no quantification or speculative decoding required
• Technical Breakthrough: DTP architecture hides All-Reduce communication delay in the computing pipeline, and the efficiency is close to the ideal model of zero communication delay
• For Agent Developers: Coding Agents reduced from 5 minutes to 10 seconds per iteration

Background and trigger events

Kog AI is a startup focused on inference optimization for AMD GPUs. Yesterday (May 29), the company published three articles simultaneously on its official blog, fully disclosing the technical details of its inference engine:

1. Result Preview: 8-card AMD MI300X node, small batch inference reaches 3,000 tok/s
2. Architecture Paper: DTP (Delayed Tensor Parallelism), a new architecture that hides communication delays in the computing process
3. Engineering Deep Dive: monokernel build process on AMD MI300X

The three articles received 203 points and 91 comments on Hacker News, and the response from the community was overwhelming. The engine only supports AMD GPUs and is currently available as a technology preview via kog.ai.

Technology Breakthrough: How the DTP Architecture Works

The bottleneck of traditional LLM inference is the communication overhead of tensor parallelism. Each layer of Transformer needs to synchronize All-Reduce, and the communication cost between GPUs increases linearly with the expansion of the cluster.

Kog's DTP architecture adopts a counter-intuitive design: Deferred All-Reduce synchronization. The core idea is to let each device independently calculate the local intermediate results first, package the communication operations into asynchronous background tasks, and then merge them at the beginning of the next Transformer layer. In this way, communication delays are "buried" in the gaps in the computing pipeline and no longer become a stagnation factor.

Single-core continuous decoding (Monokernel)

In addition to DTP, Kog implements the entire LLM decode pass as a single persistent kernel. This means:

• No need to repeatedly start/destroy kernels on GPU
• The weight flow goes directly from HBM to the computing unit with very few intermediate links.
• All intermediate results are kept in GPU SRAM, reducing video memory accesses

On AMD MI300X, the combination of monokernel + DTP enables the 2B model to reach 3,000+ tok/s at batch size 1.

Impact on AI Agent developers

**Real-time inference is not just faster responses, but a new interaction paradigm. **

For AI coding agents (such as Claude Code, Cursor, Copilot), the most painful thing currently is that the iteration cycle of each modification → feedback is usually 1-5 minutes. Kog's inference engine compresses this cycle to less than 10 seconds:

 (10s) → (60-300s) → →
Kog (10s) → (~3s) → →

3,000 tok/s 200 token 67ms， 1,000 token 333ms。 Agent "，"——。

HN

• **** monokernel AMD GPU ， 2-3
• **** 2B ChatGPT （），3,000 tok/s 2B ， 70B+
• **** AMD MI300X， NVIDIA H100/B200

AI

1. ** MoE **Kog ，MoE （） batch size 1 dense 。 Kog MoE， 4B active params
2. **AMD **MI300X NVIDIA， AMD 2026
3. ****3,000 tok/s LLM （、），
4. ** Kog ** kog.ai ，

• Kog
• DTP
• AMD MI300X monokernel
• Kog AI

，AMD MI300X、Claude Code、ChatGPT、Cursor

Internal link guidance

• Want to learn how to build an AI coding agent workflow? See: How to choose AI programming Agent? Three-dimensional comparison of language, model and cost.
• Real case: An independent developer earns over 10,000 yuan a month using AI coding tools——Claude Code 48 hours to start a business: one person + US$29 monthly fee, monthly income in 3 months $9,000