Local vs cloud showdown is heating up for 2025 LLM deployment. NVIDIA DGX Spark and Apple Mac Studio promise about 4x faster inference with EXO 1.0, though real wins depend on prompts and task type [1].
The hardware debate isn’t just hype. In practice, teams weigh cloud credits vs local machines vs dedicated accelerators:
GCP credits vs MacBook Pro 5 vs NVIDIA DGX Spark — cloud scale is enticing, but some setups favor a capable on-site rig for predictability and development cycles [3].
On the hardware front, early-experimenters point to local hosting on premium GPUs, laptops, or compact clusters depending on workflow. The tradeoffs show up in cost, maintenance, and how you shard work between prefill and decode phases [1][3].
Benchmark snapshot: RTX Pro 6000 Blackwell with vLLM handles 120B models with impressive multi-user scaling. Peak throughput hits 1051 tok/s at 1K context and holds 300-476 tok/s with 20 users; latency ranges from 2.6s to about 30s as context grows, with 96GB VRAM headroom preventing swapping at 128K context [4].
Open, cost-efficient 3B/8B models are shifting the economics. Schematron-8B and Schematron-3B rival GPT-5 for HTML extraction while costing 40-80x less; Schematron-8B can scrape pages in ~0.54s vs ~6s for frontier models [5].
Bottom line: match the path to your workload—cloud credits for scale, high-end local GPUs for multi-user throughput, or open 3B/8B models for lean, cost-effective tasks [5].
References
Nvidia DGX Spark and Apple Mac Studio = 4x Faster LLM Inference with EXO 1.0
EXO 1.0 on DGX Spark boosts LLM inference; discussions cover prefill vs decode, model sizes, costs, and future cloud tradeoffs.
View source[D] GCP credits vs mac book Pro 5 vs Nvidia DGX?
Discusses DGX, GCP credits, and MacBook trade-offs for running open/closed LLMs and multimodal models; focuses on inference speed and cost.
View sourceRTX Pro 6000 Blackwell vLLM Benchmark: 120B Model Performance Analysis
Benchmarks RTX Pro 6000 Blackwell with vLLM on gpt-oss-120b show throughput, latency, multi-user scaling, MoE benefits, and price debates.
View sourceWe built 3B and 8B models that rival GPT-5 at HTML extraction while costing 40-80x less - fully open source
Small Schematron models outperform in HTML-to-JSON extraction; cheaper and faster than GPT-5; open-source, large context support for web scraping workloads.
View source