Faster

Inference

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Enabling AI coding agents and agentic workflows to generate 4,000 tokens per second per request (vs ≈ 100 tok/s for ChatGPT)

>The problem

Deep reasoning.
Instant results.

Longer Chain-of-Thoughts improves accuracy. The research confirms it, and the scaling laws back it up.

The infrastructure to deliver that reasoning depth at product speed did not exist. Kog built it.

Your agents now think longer, answer better, and respond before the wait becomes friction.

min

/ cycle

Standard latency

Draft

Refine

Test

Lint

sec

/ cycle

Kog Velocity

>The Solution

40 refinement loops.
One standard run

From 5 minutes to 6 seconds

At 4,000 tokens per second, each iteration cycle drops from 5 minutes to 6 seconds.

Your agent drafts, lints, tests, and refines. You think. The cycle runs 40 times in the time a standard stack completes one.

This is the threshold where agent tooling becomes agent thinking. The product you build on the other side redefines what exists today.

>our Technology

The Kog Inference Engine

A hardware-software co-design built to run the GPU at its absolute ceiling. Every layer is optimized for one outcome, compute that runs without interruption.

Kog Communication
Library (KCCL)

We replaced standard communication layers (RCCL) to unlock linear scaling for tensor parallelism across high-end GPUs.

KCCL

NCCL (new)

RCCL

NCCL (old)

Latency (µs)

10000

12500

15000

17500

20000

Operation Size (Bytes)

Kog LaneFormer

Inter-device communication is delayed by one layer. Compute runs continuously. The generation sequence completes without a single synchronization pause.

Hardware-native

Precise access patterns reduce latency for device-wide primitives. Topology-aware algorithms steer every memory access toward its optimal physical location on the GPU. Most frameworks guess. Kog knows.

Zero Friction

A drop-in replacement for vLLM. No code refactoring required. Fully compatible with your existing container ecosystem."

Container

vLLM Interface

Compatible with your current containers and vLLM Interface

>The proof

Same Silicon,
Same Model,

0.0

x

faster

We pitted the Kog Inference Engine (KIE) against the industry standard (vLLM or TensorRT-LLM).

The result? Up to 3.5x on AMD instinct, faster generation on identical workloads. KIE extracts the raw physics of the GPU where standard frameworks hit a software ceiling.

See benchmark