ATLAS

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\title{\textbf{ATLAS Engine: Redefining LLM Inference Efficiency}} \author{Project Story} \date{2026}

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\section*{Inspiration} The inspiration for ATLAS arose from the "Inference Tax"—the performance gap between theoretical hardware capabilities and actual LLM throughput. While existing frameworks like vLLM provide paged attention, we aimed to build a "vLLM-Killer" by optimizing the stack at the kernel level, removing the overhead of generic deep learning operators.

\section*{What it does} ATLAS is a high-performance inference engine designed to squeeze every TFLOP out of a GPU. It utilizes a \textbf{Paged KV Cache} to prevent memory fragmentation and executes attention via custom \textbf{Triton kernels}. The engine tracks real-time metrics, including P99 latency and tokens-per-second, ensuring production-grade reliability.

\section*{How we built it} The architecture is centered around deep hardware integration: \begin{itemize} \item \textbf{Paged KV Cache:} We implemented \texttt{PagedKVCache} to manage memory in discrete blocks, using a \texttt{block_tables} mapping system to store keys and values non-contiguously. \item \textbf{Fused Triton Kernels:} We authored custom kernels for prefill and decode phases. The attention mechanism follows: \begin{equation} \text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V \end{equation} Our \texttt{fused_decode_attention_kernel} merges the attention calculation with the KV cache write operation to minimize global memory round-trips. \item \textbf{Benchmark-Driven Autotuner:} A \texttt{BenchmarkAutotuner} class profiles the GPU to find the optimal \texttt{BLOCK_M} and \texttt{BLOCK_N} configurations dynamically. \end{itemize}

\section*{Challenges we ran into} The primary challenge was managing the \textbf{Memory Wall}. Implementing the \texttt{blockwise_prefill_kernel} required precise pointer arithmetic to map sequence positions to block addresses within the Triton JIT environment while maintaining causal masking for $T > 1$.

\section*{Accomplishments that we're proud of} We successfully achieved a native implementation that matches or outperforms vLLM baselines. Our engine delivers over 45,000 tokens per second on optimized batches, maintaining stable P99 latencies even under heavy load.

\section*{What we learned} We learned that the key to inference speed isn't just raw compute—it's memory orchestration. Moving logic from Python to \texttt{triton.jit} kernels reduced CPU overhead significantly, and Paged Attention proved essential for handling high-concurrency workloads.

\section*{What's next for ATLAS} The future of ATLAS includes implementing continuous batching, adding support for FP8 quantization kernels, and extending the \texttt{ProductionLLMEngine} to support multi-GPU Tensor Parallelism to handle models beyond 70B parameters.

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Built With

• benchmarkautotuner
• blockwise-prefill-kernel
• groupedqueryattention
• productionllmengine
• python
• rotaryembedding
• transformerlayer