Introduction

Modern generative AI models like LLaMA-1B have traditionally relied on inference engines that execute their forward passes as hundreds of small CUDA kernels. Systems like vLLM and SGLang achieve competitive speeds, but at a cost: the GPU sits idle between kernels as weights load, leading to underutilized bandwidth and noticeable per-inference latency.

A recent innovation using megakernels has changed this paradigm entirely. Researchers at Stanford (Benjamin Spector*, Jordan Juravsky*, et al.) have demonstrated that by fusing an entire forward pass into a single GPU kernel (a “megakernel”), we can drop the LLaMA-1B forward pass latency to under 1 ms on an H100, with GPU bandwidth usage jumping from <50% to ~78% and speedups of up to 2.5× vs vLLM and 1.5× vs SGLang. B200s push this further to 0.68 ms.

Why Megakernels Matter

• No more per-layer stalls. Steps like RMS normalization, QKV projection, RoPE embedding, attention, MLP feed-forward, and logits computation happen in one pass.
• On-GPU interpreter. Shared-memory pages dynamically swap between these steps. Lightweight dependency tracking with on-chip counters eliminates full-GPU barriers.
• Maximal hardware utilization. Eliminating CPU-host syncs lets the GPU saturate bandwidth, boosting throughput and reducing power.
• Responsiveness at scale. Even when serving thousands of sessions concurrently, a single H100 can stay ahead of the queue.

Simplismart Integration: Supercharging Our Inference Stack

At Simplismart, our core focus is delivering ultra-fast, cost-efficient inference. Megakernels fit perfectly into this story:

1. Seamless Engine Support: Our Serving Layer can register a megakernel-based engine as a first-class inference backend and we’ll auto-deploy these fused executables for supported models.
2. Transparent Benchmarking: Our benchmarking suite provides apples-to-apples comparisons of megakernels vs. baseline inference engines across latency, cost, and power.
3. Customer Impact: Users in regulated industries can deploy on-premises megakernel engines with Simplismart and eliminate the “thinking…” wait entirely. Applications like legal drafting, coding copilots, or customer support can now feel instantaneous even at 32K+ context windows.

What’s Next

Our roadmap includes:

• Extending megakernel support to other popular model families (Mistral, Qwen, etc.), with tailored kernel shapes per model architecture.
• Automating kernel generation during fine-tuning so users get optimal kernels after every LoRA or QLoRA adaptation.

Closing thoughts

With megakernels, inference ceases to be a bottleneck. By embedding this research into our platform, Simplismart customers can deploy models that respond almost instantly, unlock new UX patterns, serve vastly more concurrent sessions per GPU, and cut their inference bills. Let’s make instant AI a reality.

Citations:
[1] Stanford.edu