DigitalOcean Gradient™ AI GPU Droplets Optimized for Inference: Increasing Throughput at Lower the Cost

DigitalOcean Gradient™ AI GPU Droplets Optimized for Inference: Increasing Throughput at Lower the Cost Prefill, Decode, and Why Optimization is Multiplicative The Optimization Stack Speculative Decoding FP8 Quantization Flash Attention-3 and Paged Attention Concurrent Optimization Prompt Caching Benchmark Methodology Test Results Throughput: 143% Improvement over Baseline Time-to-First-Token: 40.7% Reduction Compared to Baseline Cost Efficiency: 75% Reduction Compared to Baseline Throughput vs. Concurrency Cost per Million Tokens vs. Concurrency From 4 GPUs to 2: The Infrastructure Efficiency Story What This Means for Inference at Scale About the author(s) Try DigitalOcean for free Related Articles LLM Inference Benchmarking - Measure What Matters Technical Deep Dive: How we Created a Security-hardened 1-Click Deploy OpenClaw Technical Deep Dive: How DigitalOcean and AMD Delivered a 2x Production Inference Performance Increase for Character. ai By Jason Peng and Hemasumanth Rasineni Updated: February 19, 2026 11 min read 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. $5.80) — all while running Llama 3.3 70B on 2 H100 GPUs instead of 4.