Gradient checkpointing is an optimization that reduces the memory footprint by re-computing some operations instead of saving their activations. Previous works on checkpointing have viewed this as a tradeoff between peak memory and performance. However, we argue that this framing does not account for a key aspect of modern deep learning systems -- operator fusion. In this work, we demonstrate that with a fusion aware checkpointing algorithm, we can transcend the runtime-memory tradeoffs of traditional checkpointing and improve both memory and runtime simultaneously. We evaluate our algorithm on a wide range of standard neural network models as well as some novel patterns. We achieve a geomean of 12% throughput improvement over an existing compiled baseline, and the maximum batch size that can be attained is up to 1.75 times larger on standard models. In novel patterns, we achieve up to a 10x improvement, with by a 5x reduction in peak memory.