• By Jason Peng and Hemasumanth Rasineni Production-grade LLM inference demands more than just access to GPUs; it requires deep optimization across the entire serving stack, from quantization and attention kernels to memory management and parallelism strategies. • Most teams deploying models like Llama 3.3 70B on vanilla configurations are leaving the majority of their hardware’s capability on the table: underutilized FLOPs, wasted memory bandwidth, and GPU hours spent waiting instead of computing. • To solve this, we built the Inference Optimized Image a fully pre-configured OS image available on DigitalOcean’s GPU Droplets - that layers speculative decoding, FP8 quantization, FlashAttention-3, paged attention, concurrent optimization, and prompt caching into a single deployable image. • The result of our particular test: 143% higher throughput (2,000 vs. • 823 tokens/second), 40.7% lower TTFT (187.9ms vs. • 316.83ms), and a 75% reduction in cost per million tokens ($1.472 vs.

Article Summaries:

  • By Jason Peng and Hemasumanth Rasineni Production-grade LLM inference demands more than just access to GPUs; it requires deep optimization across the entire serving stack, from quantization and attention kernels to memory management and parallelism strategies. Most teams deploying models like Llama 3.3 70B on vanilla configurations are leaving the majority of their hardware’s capability on the table: underutilized FLOPs, wasted memory bandwidth, and GPU hours spent waiting instead of computing. To solve this, we built the Inference Optimized Image a fully pre-configured OS image available on D

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