Massed Compute vs Salad

Massed Compute employs per-hour billing for on-demand VMs, providing cost predictability for sustained usage but less flexibility for short or intermittent jobs—minimum charges apply per hour, suiting steady remote sessions. Salad uses per-second billing with spot instances, enabling precise pay-for-use and aggressive discounts (often 50-80% below on-demand), alongside potential reserved options in its decentralized pool. Implications vary: Massed minimizes billing overhead for long runs (e.g., 24/7 workstations), while Salad optimizes for bursty patterns like overnight training, where preemptions can slash costs but introduce retries. No reserved instances noted for Massed; Salad's spot model risks interruptions, favoring fault-tolerant apps. Short experiments (<1 hour) heavily favor Salad; prolonged interactive use aligns with Massed.

Salad delivers superior value for large training runs and batch inference, where per-second spot pricing yields 3-5x savings on consumer GPUs for TB-scale datasets, offsetting variability via fault tolerance. Massed Compute offers better value for production real-time inference or fine-tuning, with reliable high-end GPUs justifying per-hour premiums through uninterrupted performance and ThinLinc access. Small experiments favor Salad's low entry barrier (pay only for seconds used), while extended remote workstations suit Massed to avoid spot preemptions. For budget-constrained teams scaling to 100s of GPUs, Salad wins; reliability-focused setups with <10 GPUs prefer Massed. Overall, Salad maximizes ROI for volume, Massed for quality.

Massed Compute provides virtualized high-performance VMs on dedicated hardware, emphasizing bare-metal-like GPU passthrough, fast NVMe storage, and low-latency networking for remote access; ThinLinc enhances desktop UX. Kubernetes support uncertain. Salad's decentralized model aggregates consumer GPUs from residential nodes, offering virtualized spot instances with distributed storage (e.g., S3-compatible); strong Kubernetes integration via fault-tolerant orchestration, but networking varies by node locality. Massed prioritizes consistency; Salad scales massively via peer network.

Massed Compute delivers consistent high-end GPU performance (e.g., A100/H100 equivalents) with excellent multi-GPU scaling and low remote latency via ThinLinc, ideal for simulations. Salad's consumer GPUs (RTX 30/40 series) offer variable throughput—strong for parallel batch but prone to preemptions and heterogeneity, impacting tight scaling. Multi-GPU works via network fabrics in Salad for fault-tolerant jobs; Massed excels in cohesive clusters. Availability high for both, but Salad's pool size dwarfs Massed's boutique offerings.