Quick Comparison: 5 Platforms at a Glance
The GPU rental market has matured significantly. Multiple platforms now let hardware owners monetize idle GPUs — but each serves a different profile of provider. Before diving into individual reviews, here is the landscape:
| Platform | Best For | Platform Fee | Min GPU | Payout | Setup Time |
|---|---|---|---|---|---|
| Vast.ai | Multi-GPU setups, technical users | 10–15% | 8 GB VRAM | Crypto wallet | 15–30 min |
| RunPod | Community-friendly, easy setup | 15–20% | 16 GB VRAM | Crypto / credits | 10–20 min |
| Salad | Consumer GPUs, minimal effort | 20–25% | 6 GB VRAM | Bank transfer | 5–10 min |
| Lambda Cloud | Enterprise GPUs, high-end providers | Negotiated | A100+ | Bank transfer | 30–60 min |
| CoreWeave | Data center operators, bulk capacity | Negotiated | Enterprise-grade | Bank transfer | Days–weeks |
Key insight: No single platform is “best” for everyone. Vast.ai offers the lowest fees but requires technical skill. Salad is the easiest to start but takes the largest cut. Lambda and CoreWeave pay the highest rates but only accept enterprise-grade hardware.
Platform 1: Vast.ai
Best for: Technical users with multi-GPU setups, ex-miners, and providers seeking maximum control over pricing.
Vast.ai operates as a peer-to-peer marketplace where providers set their own pricing and renters bid on available machines. This creates a transparent, competitive market — but also means your earnings depend on your ability to price competitively.
Key features:
- Lowest platform fee in the market (10–15%)
- Provider sets their own hourly rate
- Real-time supply and demand visibility
- Docker-based workload isolation
- Support for multi-GPU machines
Earnings potential:
- RTX 3090: $0.40–$0.80/hr gross
- RTX 4090: $0.55–$1.50/hr gross
- A100 80GB: $1.50–$3.00/hr gross
Strengths: Transparent marketplace, lowest fees, provider pricing control, strong multi-GPU support.
Weaknesses: Requires Docker and Linux experience, variable demand (utilization fluctuates), community-based support (no enterprise SLA), reputation system can disadvantage new providers initially.
Setup process: Install NVIDIA drivers → Install Docker → Download Vast.ai CLI → Register machine → Set pricing → Go live. Requires comfort with command-line Linux administration.
For our detailed analysis of Vast.ai’s pricing, reliability, and alternatives, see the Vast.ai review.
Platform 2: RunPod
Best for: Community-oriented providers seeking a balance between ease of use and competitive earnings.
RunPod has grown rapidly as a developer-friendly GPU cloud that supports both renters and providers. The platform handles much of the infrastructure complexity, making it easier to get started than Vast.ai while still offering reasonable returns.
Key features:
- Serverless and pod-based workload options for renters (drives demand to your hardware)
- Streamlined provider onboarding
- Community marketplace with active demand
- Automatic pricing suggestions based on market rates
- Dashboard with utilization and earnings analytics
Earnings potential:
- RTX 4090: $0.50–$1.20/hr gross
- A100 80GB: $1.50–$2.50/hr gross
- H100: $2.50–$4.00/hr gross
Strengths: Growing community demand (strong utilization), good documentation, provider-friendly dashboard, supports both consumer and enterprise GPUs.
Weaknesses: Higher fees than Vast.ai (15–20%), minimum 16 GB VRAM excludes lower-end GPUs, less pricing flexibility than a pure marketplace model.
Setup process: Create provider account → Install RunPod agent → Run hardware benchmark → Configure availability schedule → Go live. More guided than Vast.ai with less manual configuration.
Platform 3: Salad
Best for: Consumer GPU owners who want the simplest possible setup with minimal technical knowledge.
Salad takes the opposite approach from Vast.ai — it abstracts away all complexity. You download a desktop app, click “start,” and your GPU begins earning. The platform handles pricing, workload matching, and payment automatically.
Key features:
- Desktop app — no Docker, no CLI, no Linux required
- Works on Windows with consumer GPUs
- Automatic pricing (no provider pricing decisions)
- Lowest minimum GPU requirement (6 GB VRAM)
- Bank transfer payouts
Earnings potential:
- RTX 3060 (12 GB): $0.05–$0.15/hr gross
- RTX 3080: $0.15–$0.30/hr gross
- RTX 4090: $0.30–$0.60/hr gross
Strengths: Easiest setup in the market (5 minutes), works on Windows, no technical knowledge required, accepts the widest range of GPUs.
Weaknesses: Highest platform fee (20–25%), lowest gross earnings per GPU (platform keeps more, passes less to providers), no pricing control, limited to workloads Salad chooses to route to you.
Setup process: Download Salad app → Sign in → Click “Start” → Earn. Genuinely a 5-minute process.
Trade-off: Salad’s simplicity comes at a cost. For an RTX 4090, Vast.ai providers earn $0.55–$1.50/hr gross at 10–15% fee, while Salad providers earn $0.30–$0.60/hr at 20–25% fee. Over a month, that is roughly $100–$300 less earnings on Salad for the same hardware.
Platform 4: Lambda Cloud
Best for: Enterprise GPU owners with A100 or H100 hardware seeking premium rates and reliable demand.
Lambda Cloud focuses on the high end of the market. It serves AI researchers and companies with managed GPU cloud infrastructure, and it sources capacity from qualified providers with enterprise-grade hardware.
Key features:
- Premium pricing for enterprise GPUs
- Managed infrastructure reduces provider burden
- Strong demand from AI research community
- Dedicated support for providers
- Long-term capacity agreements available
Earnings potential:
- A100 80GB: $2.00–$3.50/hr gross
- H100: $3.50–$5.00/hr gross
Strengths: Highest per-GPU rates for enterprise hardware, reliable demand from established AI companies, professional support, long-term contracts provide income stability.
Weaknesses: Consumer GPUs not accepted, significant hardware requirements (data center-grade cooling, networking, redundancy), longer onboarding process with hardware verification, negotiated terms (not transparent marketplace pricing).
Setup process: Apply as provider → Hardware verification → Infrastructure audit → Capacity agreement → Deployment. This is a weeks-long process for new providers.
Platform 5: CoreWeave
Best for: Data center operators with significant GPU capacity seeking wholesale-level partnerships.
CoreWeave is a GPU-native cloud provider that builds its infrastructure from distributed GPU capacity. For operators with racks of enterprise GPUs, CoreWeave offers the highest utilization rates and most stable demand — but the barrier to entry is the highest in the market.
Key features:
- Wholesale capacity agreements for large providers
- Kubernetes-native infrastructure
- Highest utilization rates in the market
- Enterprise SLAs backed by CoreWeave
- Co-location and bare-metal options
Earnings potential:
- Bulk A100 capacity: $1.50–$2.50/hr per GPU (at scale)
- Bulk H100 capacity: $3.00–$4.50/hr per GPU (at scale)
Strengths: Most stable demand and utilization, enterprise-grade SLAs, highest total earnings for large providers, professional infrastructure partnership.
Weaknesses: Not accessible to individual GPU owners, requires data center-scale operations, complex onboarding (weeks to months), minimum capacity commitments.
Setup process: Business partnership application → Infrastructure assessment → Contract negotiation → Physical deployment or network integration. Enterprise sales process.
Head-to-Head Comparison Table
How to Choose the Right Platform for Your Hardware
The decision depends on three factors: what GPU you have, how technical you are, and how much effort you want to invest.
If You Have Consumer GPUs (RTX 3060–5090)
Want zero effort? → Salad. Download the app, click start. Lower earnings but zero complexity.
Want maximum earnings? → Vast.ai or RunPod. Higher earnings per GPU, but requires Docker and Linux knowledge. Vast.ai’s lower fees make it the better choice if you are comfortable with the command line.
If You Have Professional GPUs (A6000, A100)
Want reliable demand? → RunPod or Lambda Cloud. Both have strong AI community demand and consistent utilization for professional-tier hardware.
Want pricing control? → Vast.ai. Set your own rates and compete in the transparent marketplace. Best for providers who actively manage their listings.
If You Have Enterprise GPUs (H100, multiple A100s)
Single machine? → Vast.ai or RunPod. Both support enterprise GPUs from individual providers.
Multiple machines / rack-scale? → Lambda Cloud or CoreWeave. These platforms are designed for bulk capacity providers and offer the highest total earnings at scale.
The Multi-Platform Strategy
Many experienced providers list on 2–3 platforms simultaneously (where terms of service allow), routing hardware to whichever platform offers the best utilization at any given time. This diversifies income and protects against platform-specific demand fluctuations.
If you have hardware to contribute, you can become a GPU provider on GPUnex to monetize your idle GPUs with transparent pricing and growing demand. Alternatively, if you prefer a hands-off approach, GPU packages start at $59 and let you earn daily revenue from compute demand without managing any hardware.
For a step-by-step guide on setting up your GPU for rental — including readiness checklists, profit formulas, and hidden costs — see our GPU rental passive income guide. For understanding the broader economics of GPU ownership, see our rent vs. buy analysis. For a broader look at GPU rental as one of five paths to participating in AI infrastructure, see our AI infrastructure guide.
Frequently Asked Questions
Which platform pays the most per GPU?
Lambda Cloud and CoreWeave offer the highest per-GPU rates — but only for enterprise hardware (A100, H100). For consumer GPUs, Vast.ai offers the best net earnings due to its lowest platform fee (10–15%). RunPod is a close second. Salad pays the least per GPU but requires the least effort.
Can I use multiple platforms at once?
Check each platform’s terms of service. Some allow simultaneous listing on other platforms when your GPU is idle. Others require exclusivity. In practice, many providers list on 2–3 platforms and route capacity based on where demand is strongest.
How long until I see meaningful earnings?
Most platforms require a 1–3 week ramp-up period while your provider reputation builds. First-month earnings are typically 30–40% of steady state. By month 3, utilization stabilizes and earnings become more predictable. Enterprise platforms (Lambda, CoreWeave) may take longer to onboard but provide more stable demand from day one.
What happens if a renter damages my hardware?
All major platforms run workloads in containers (Docker) that isolate the renter’s code from your system. This prevents direct hardware access or damage. However, sustained heavy workloads do accelerate normal wear (fan bearings, thermal paste). Platforms do not generally cover hardware degradation from normal compute use.
Do I need a static IP or dedicated internet?
A static IP is not required by most platforms, but a stable connection with consistent upload speed (50+ Mbps) is essential. Frequent disconnections hurt your uptime score, which reduces workload allocation. A dedicated internet connection (not shared with heavy household use) is strongly recommended for reliable provider operation.