When diving into deep learning, one of the first major decisions is selecting the right GPU. The market offers a spectrum of options, from consumer-grade cards to high-end professional GPUs. Two names that frequently surface in these discussions are the NVIDIA RTX 4070 and the A100. While the A100 is known for its hefty price tag of around $12,000 (if you can even find one available), the RTX 4070 presents a more accessible alternative, typically priced around $1800 (and often less for similar performance tiers in the 40-series). For tasks demanding massive 80GB VRAM, the A100 stands almost unchallenged, save for options like the NVIDIA L40 with 48GB VRAM, which, while still costly, might be a more economical choice than the A100. The L40, essentially a professional-grade RTX 4090, offers comparable performance with slightly reduced memory bandwidth.
A frequently underestimated aspect in GPU performance is the L2 cache size. Consider the A40 with a mere 6MB of L2 cache – surprisingly low, especially for a card often deemed bandwidth-starved with 700GB/s compared to the RTX 3090’s 940 GB/s or the A100’s impressive 1950 GB/s. The A100 boasts a substantial 80MB of L2 cache, while the RTX 4090 ups the ante with 72MB, and the L40 leads with a staggering 96MB. This L2 cache is the fastest memory resource available on the GPU, and for memory-intensive operations like FFTs and potentially sorting algorithms, its size can be more critical than raw memory bandwidth. Therefore, if 24GB of memory is sufficient for your deep learning tasks, the RTX 4090 (or by extension, the RTX 4070 with adjustments for its memory and processing power) becomes a compelling option. For those willing to invest more, the L40 emerges as a strong contender, possibly even more so than the A100 or the newer H100, depending on the specific workload.
It’s also crucial to analyze the specific demands of your deep learning workflows. Focusing on a sorting algorithm example, tests reveal intriguing insights. The A40 processes a chunk of data (around 53,000 atoms) in approximately 32 microseconds, while the A100 achieves 29 microseconds per chunk. Despite the A100 offering 2.7x the memory bandwidth and 13x the global cache space of the A40, the performance gain in this specific sorting task is marginal. Interestingly, an older RTX 2080Ti, generally slower than both the A40 and A100 in general compute tasks, completes the same sorting task in just 36 microseconds – only 12-25% slower. This suggests that for certain algorithms, performance plateaus beyond a certain point, implying the sorting process isn’t solely L2-cache bound. While a global cache is beneficial, the bottleneck might lie in the speed of data transfer to the __shared__ memory partition of L1 cache and subsequent data rearrangement. This aspect of GPU architecture may have seen incremental improvements over recent years, with advancements focusing on increasing GPU size (more Streaming Multiprocessors – SMs) rather than significantly boosting the speed of individual SMs in accessing global resources. The memory bus bandwidth, while growing, tends to scale proportionally with the number of SMs.
When choosing between an RTX 4070 and an A100 for deep learning, consider the balance between cost, memory requirements, and the nature of your computational tasks. The RTX 4070 offers a cost-effective entry point into powerful GPU computing, suitable for many deep learning projects, especially those that are not heavily reliant on massive datasets exceeding its VRAM capacity. The A100 remains a top-tier choice for researchers and professionals tackling the most demanding models and datasets, where its large VRAM and robust architecture justify the significant investment. However, for many practical deep learning applications, the RTX 4070, or similar cards in its class, can provide an optimal blend of performance and affordability.
