Cloud vs. Local GPU for LLM: Performance & Cost Analysis

Ever wondered if renting cloud GPUs is more cost-effective than using local hardware for your LLM tasks? With local GPU prices reaching new heights and AI models growing in complexity, this comprehensive analysis explores the performance and cost considerations to help you make an informed decision. When deciding between cloud and local GPUs for LLM work, several factors come into play: Local GPU advantages: Always available Not dependent on internet speed Electricity is the only variable cost Local GPU disadvantages: High upfront investment Limited performance ceiling May become outdated quickly Cloud GPU advantages: No extra power bills Access to higher-end hardware On-demand upgrading Pay-as-you-go pricing Cloud GPU disadvantages: Ongoing costs Internet dependency Potential queue times Some situations leave you with no alternatives: Privacy requirements: If working with confidential data, local GPUs may be your only option, though providers differ in their privacy policies. VRAM requirements: Some models require more than 32GB VRAM (beyond even what a 5090 offers), making cloud GPUs necessary. Time constraints: Strict deadlines may necessitate highest-performance options without building your own GPU server farm. To compare GPUs objectively, we used two benchmarks: ComfyUI GPU benchmark - Nearly 100% GPU workload when iterating sampling steps Hunyuan Video Generation - Tests both GPU and VRAM under real-world conditions Tests were conducted on Lightning AI's platform, which offers various GPU types at different price points. Power consumption was measured using a dedicated device and correlated with published TGP (Total Graphics Power) specifications. ComfyUI Benchmark: Among local GPUs, the RTX 4090 delivers highest performance The RX 6800 struggles compared to other options The newer RX 7900 XTX performs significantly better In cloud solutions, the L40S (48GB VRAM) outperforms even the 4090 Hunyuan Video Benchmark: Cloud GPUs generally outperform local options except for the T4 The L40S delivers exceptional performance for VRAM-intensive tasks Many 8GB GPUs struggle to render videos completely Hourly Costs: Local GPU costs calculated based only on electricity consumption Cloud GPUs have fixed hourly rates Initial comparison suggests cloud GPUs are significantly more expensive Cost Per Workload: For image generation with SDXL, the RTX 4080 offers the best price-performance ratio For VRAM-intensive video generation, cloud L4 and L40S deliver better value than some local options like the RX 6800 The T4 cloud option proves too weak for its price point Consider the total cost of ownership: A 5090 would need to generate approximately 65,000 benchmark videos to break even against cloud options For higher-resolution 720p videos (costing around 50 cents each), the breakeven point would be around 5,000 videos If you only use AI occasionally, renting cloud GPU time is more economical than purchasing expensive hardware. However, frequent AI users will find local GPUs more cost-effective, assuming they meet your performance and VRAM requirements. BlackSkye offers an innovative solution to this dilemma through their decentralized GPU marketplace, connecting users needing compute power with providers offering idle GPU resources. Their real-time pricing and pay-per-job billing provide flexibility that bridges the gap between traditional cloud services and owned hardware.