Kog vs. the GPU Ceiling: How Software Alone Can Deliver 30x Faster LLM Inference
Kog achieves 3,000 output tokens/s per request on an 8× AMD MI300X node using a purely software driven inference engine — no custom hardware, no quantization, and no speculative decoding — by co designing a custom mod... Its core innovation — Delayed Tensor Parallelism (DTP) — delays inter GPU all reduce by 2 layers...
Published byEdited with DeepSeek-V4-FlashImages generated with GPT Image 1.5
Kog achieves 3,000 output tokens/s per request on an 8× AMD MI300X node using a purely software driven inference engine — no custom hardware, no quantization, and no speculative decoding — by co designing a custom mod...
Its core innovation — Delayed Tensor Parallelism (DTP) — delays inter GPU all reduce by 2 layers to overlap communication with computation, eliminating the stalls that plague standard tensor parallelism.
Kog has yet to demonstrate its full 30x claim at scale (its best demo is on a 2B model, not a frontier 70B+ model); the next critical proof point will be a 10x speed improvement on a major industry scale model in the...
What is Kog's approach to achieving up to 30x faster LLM inference on existing GPUs, how does its software-first strategy work, what resultsKog's software-only strategy eliminates GPU stalls through co-designed model architecture and kernel design.
AI Prompt
Create a landscape editorial hero image for this Studio Global article: What is Kog's approach to achieving up to 30x faster LLM inference on existing GPUs, how does its software-first strategy work, what results. Article summary: ## Kog's Approach and Results. Topic tags: general, documentation, general web, user generated, academic. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks, charts with fake numbers, clickbait thumbnails, icons, and tiny thumbnail layouts. Make it useful as an illustrative visual, not as factual evidence.
openai.com
Kog is a French startup that claims it can achieve up to 30x faster LLM inference on existing datacenter GPUs through a deep, software-first optimization stack — co-designing a custom model architecture (Laneformer), a single-kernel inference engine (KIE), and a custom communication layer (KCCL) to eliminate every source of stall in the decode loop.
Rather than building custom ASICs or acquiring exotic hardware, Kog treats three layers as one co-designed system:
Monokernel (single persistent CUDA kernel): The entire LLM decode pass runs as one persistent kernel with no kernel launches or interruptions between layers, removing GPU launch overhead entirely.
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What is the short answer to "Kog vs. the GPU Ceiling: How Software Alone Can Deliver 30x Faster LLM Inference"?
Kog achieves 3,000 output tokens/s per request on an 8× AMD MI300X node using a purely software driven inference engine — no custom hardware, no quantization, and no speculative decoding — by co designing a custom mod...
What are the key points to validate first?
Kog achieves 3,000 output tokens/s per request on an 8× AMD MI300X node using a purely software driven inference engine — no custom hardware, no quantization, and no speculative decoding — by co designing a custom mod... Its core innovation — Delayed Tensor Parallelism (DTP) — delays inter GPU all reduce by 2 layers to overlap communication with computation, eliminating the stalls that plague standard tensor parallelism.
What should I do next in practice?
Kog has yet to demonstrate its full 30x claim at scale (its best demo is on a 2B model, not a frontier 70B+ model); the next critical proof point will be a 10x speed improvement on a major industry scale model in the...
Kog vs. the GPU Ceiling: How Software Alone Can Deliver 30x Faster LLM Inference
Kog achieves 3,000 output tokens/s per request on an 8× AMD MI300X node using a purely software driven inference engine — no custom hardware, no quantization, and no speculative decoding — by co designing a custom mod... Its core innovation — Delayed Tensor Parallelism (DTP) — delays inter GPU all reduce by 2 layers...
Published byEdited with DeepSeek-V4-FlashImages generated with GPT Image 1.5
Kog achieves 3,000 output tokens/s per request on an 8× AMD MI300X node using a purely software driven inference engine — no custom hardware, no quantization, and no speculative decoding — by co designing a custom mod...
Its core innovation — Delayed Tensor Parallelism (DTP) — delays inter GPU all reduce by 2 layers to overlap communication with computation, eliminating the stalls that plague standard tensor parallelism.
Kog has yet to demonstrate its full 30x claim at scale (its best demo is on a 2B model, not a frontier 70B+ model); the next critical proof point will be a 10x speed improvement on a major industry scale model in the...
What is Kog's approach to achieving up to 30x faster LLM inference on existing GPUs, how does its software-first strategy work, what resultsKog's software-only strategy eliminates GPU stalls through co-designed model architecture and kernel design.
AI Prompt
Create a landscape editorial hero image for this Studio Global article: What is Kog's approach to achieving up to 30x faster LLM inference on existing GPUs, how does its software-first strategy work, what results. Article summary: ## Kog's Approach and Results. Topic tags: general, documentation, general web, user generated, academic. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks, charts with fake numbers, clickbait thumbnails, icons, and tiny thumbnail layouts. Make it useful as an illustrative visual, not as factual evidence.
openai.com
Kog is a French startup that claims it can achieve up to 30x faster LLM inference on existing datacenter GPUs through a deep, software-first optimization stack — co-designing a custom model architecture (Laneformer), a single-kernel inference engine (KIE), and a custom communication layer (KCCL) to eliminate every source of stall in the decode loop.
Rather than building custom ASICs or acquiring exotic hardware, Kog treats three layers as one co-designed system:
Monokernel (single persistent CUDA kernel): The entire LLM decode pass runs as one persistent kernel with no kernel launches or interruptions between layers, removing GPU launch overhead entirely.
Studio Global AI
Continue your research
This page includes a source-backed answer you can continue inside Studio Global.
What is the short answer to "Kog vs. the GPU Ceiling: How Software Alone Can Deliver 30x Faster LLM Inference"?
Kog achieves 3,000 output tokens/s per request on an 8× AMD MI300X node using a purely software driven inference engine — no custom hardware, no quantization, and no speculative decoding — by co designing a custom mod...
What are the key points to validate first?
Kog achieves 3,000 output tokens/s per request on an 8× AMD MI300X node using a purely software driven inference engine — no custom hardware, no quantization, and no speculative decoding — by co designing a custom mod... Its core innovation — Delayed Tensor Parallelism (DTP) — delays inter GPU all reduce by 2 layers to overlap communication with computation, eliminating the stalls that plague standard tensor parallelism.
What should I do next in practice?
Kog has yet to demonstrate its full 30x claim at scale (its best demo is on a 2B model, not a frontier 70B+ model); the next critical proof point will be a 10x speed improvement on a major industry scale model in the...
Delayed Tensor Parallelism (DTP): Standard tensor parallelism blocks on an all-reduce at every layer. Kog delays each all-reduce by δ=2 layers, overlapping communication with computation and weight streaming so the GPUs are never idle waiting for data.
Lane-structured Transformer architecture: Laneformer divides the model into 8 parallel "lanes" that align with DTP's communication schedule, allowing the engine to perfectly hide tensor-parallelism overhead.
Custom communication layer (KCCL): Replaces standard NCCL/RCCL to achieve lower-latency, linear-scaling tensor parallelism across GPUs.
Assembly-level optimization: Kog works at C++/assembly level rather than stacking tweaks on CUDA or ROCm, targeting every cycle of GPU memory bandwidth and compute utilization.
Laneformer 2B: Demonstrated Results
Kog open-sourced Laneformer 2B (2.3B parameters), an instruction-tuned coding model designed for decoding speed over raw benchmark score. These speeds are roughly 10x faster than typical vLLM throughput for a model of this size.
Metric
Result
Decode speed on 8× AMD MI300X
3,000 output tokens/s per request (FP16, batch size 1)
Decode speed on 8× NVIDIA H200
2,100 output tokens/s per request (FP16, batch size 1)
HumanEval+ (greedy)
45.1%
MBPP+ (greedy)
51.6%
Method
No quantization, no speculative decoding, no pruning
Next Milestones for Scaling
Kog's public tech preview runs on a 2B model. The company is now racing to scale its techniques:
Near-term target: Demonstrate a 10x speed improvement on a major industry-scale model (e.g., 70B+ dense or MoE). The company expects to reach this in the coming months.
Long-term ambition: Show that its approach can deliver 30x faster inference on large frontier MoE models at speeds similar to what it achieved on Laneformer 2B. Kog has publicly argued that its DTP and monokernel techniques scale favorably to MoE architectures.
Since the May 2026 launch, Kog says it has generated 200+ tangible business leads and is fully focused on larger-model support to meet enterprise demand.
Comparison to Competitors
ZML — Also a French AI startup (endorsed by Yann LeCun), ZML takes a different approach: it released LLMD, a free inference engine that allows many open-source models to run across a wide variety of chips (NVIDIA, AMD, Google TPU, Apple Metal, Intel Arc) using a unified compiler-based approach. ZML prioritizes hardware portability and multi-chip support rather than extreme single-request latency. Kog, by contrast, is purpose-built for maximum latency reduction on specific high-end datacenter GPUs (MI300X, H200) through deep, hardware-specific optimization.
Cerebras — Uses a fundamentally hardware-first strategy: the Wafer-Scale Engine (WSE-3), a massive single chip that eliminates the need for multi-GPU communication. Cerebras achieves ~2,100 tokens/s on Llama 3.1 70B (batch 1) on its WSE-3 hardware — notably, that speed is for a 70B model, not a 2B model.
Dimension
Kog
Cerebras
Approach
Software-only on standard GPUs
Custom wafer-scale hardware
Best demo
3,000 tok/s on a 2B model
~2,100 tok/s on 70B model
Latency claim
Targeting 30x on frontier models
~4x vs H100 on 70B models
Hardware cost
Uses existing GPU infrastructure
Requires proprietary CS-3 systems
Maturity
Early-stage (tech preview, 2B only)
Production-grade for multiple open models
Key caveat: Kog's 3,000 tok/s result is on a 2.3B model — an order of magnitude smaller than the 70B models Cerebras serves at comparable speeds. Kog has yet to demonstrate its 30x claim at scale. The company's next milestone (10x on a large industry model) will be the critical proof point.
Delayed Tensor Parallelism (DTP): Standard tensor parallelism blocks on an all-reduce at every layer. Kog delays each all-reduce by δ=2 layers, overlapping communication with computation and weight streaming so the GPUs are never idle waiting for data.
Lane-structured Transformer architecture: Laneformer divides the model into 8 parallel "lanes" that align with DTP's communication schedule, allowing the engine to perfectly hide tensor-parallelism overhead.
Custom communication layer (KCCL): Replaces standard NCCL/RCCL to achieve lower-latency, linear-scaling tensor parallelism across GPUs.
Assembly-level optimization: Kog works at C++/assembly level rather than stacking tweaks on CUDA or ROCm, targeting every cycle of GPU memory bandwidth and compute utilization.
Laneformer 2B: Demonstrated Results
Kog open-sourced Laneformer 2B (2.3B parameters), an instruction-tuned coding model designed for decoding speed over raw benchmark score. These speeds are roughly 10x faster than typical vLLM throughput for a model of this size.
Metric
Result
Decode speed on 8× AMD MI300X
3,000 output tokens/s per request (FP16, batch size 1)
Decode speed on 8× NVIDIA H200
2,100 output tokens/s per request (FP16, batch size 1)
HumanEval+ (greedy)
45.1%
MBPP+ (greedy)
51.6%
Method
No quantization, no speculative decoding, no pruning
Next Milestones for Scaling
Kog's public tech preview runs on a 2B model. The company is now racing to scale its techniques:
Near-term target: Demonstrate a 10x speed improvement on a major industry-scale model (e.g., 70B+ dense or MoE). The company expects to reach this in the coming months.
Long-term ambition: Show that its approach can deliver 30x faster inference on large frontier MoE models at speeds similar to what it achieved on Laneformer 2B. Kog has publicly argued that its DTP and monokernel techniques scale favorably to MoE architectures.
Since the May 2026 launch, Kog says it has generated 200+ tangible business leads and is fully focused on larger-model support to meet enterprise demand.
Comparison to Competitors
ZML — Also a French AI startup (endorsed by Yann LeCun), ZML takes a different approach: it released LLMD, a free inference engine that allows many open-source models to run across a wide variety of chips (NVIDIA, AMD, Google TPU, Apple Metal, Intel Arc) using a unified compiler-based approach. ZML prioritizes hardware portability and multi-chip support rather than extreme single-request latency. Kog, by contrast, is purpose-built for maximum latency reduction on specific high-end datacenter GPUs (MI300X, H200) through deep, hardware-specific optimization.
Cerebras — Uses a fundamentally hardware-first strategy: the Wafer-Scale Engine (WSE-3), a massive single chip that eliminates the need for multi-GPU communication. Cerebras achieves ~2,100 tokens/s on Llama 3.1 70B (batch 1) on its WSE-3 hardware — notably, that speed is for a 70B model, not a 2B model.
Dimension
Kog
Cerebras
Approach
Software-only on standard GPUs
Custom wafer-scale hardware
Best demo
3,000 tok/s on a 2B model
~2,100 tok/s on 70B model
Latency claim
Targeting 30x on frontier models
~4x vs H100 on 70B models
Hardware cost
Uses existing GPU infrastructure
Requires proprietary CS-3 systems
Maturity
Early-stage (tech preview, 2B only)
Production-grade for multiple open models
Key caveat: Kog's 3,000 tok/s result is on a 2.3B model — an order of magnitude smaller than the 70B models Cerebras serves at comparable speeds. Kog has yet to demonstrate its 30x claim at scale. The company's next milestone (10x on a large industry model) will be the critical proof point.
