GPU Comparison Engine
Select any two GPUs for a side-by-side evaluation of specs, live pricing, and cost-efficiency ratios across workload profiles.
Methodology: How GPU Comparison Works
OpenGPU Radar compares GPU instances across four dimensions: Price, Performance, Memory, and Availability. Pricing data is aggregated from Spheron, RunPod, Vast.ai, and Lambda Labs APIs, refreshed every 6 hours.
Cost Efficiency Ratio = (Baseline Spot Price) / (GPU Spot Price)\n" "A ratio > 1.0 means the GPU is cheaper than the baseline.\n\n" "Performance Index = (FP8 TFLOPS) × (Memory Bandwidth GB/s) / 1000\n" "Higher scores indicate better compute throughput per dollar.\n\n" "Example: H100 SXM5 has FP8 = 1,979 TFLOPS, BW = 3.35 TB/s\n" "Performance Index = 1979 × 3350 / 1000 ≈ 6,630"
Hardware Requirements by Workload
Minimum VRAM and GPU specs required for each workload category. Determined via entity-graph VRAM calculations.
| Workload | Min GPU | Min VRAM | Precision | Recommended GPU |
|---|---|---|---|---|
| 70B LLM Inference | H100 SXM5 | 80 GB | FP8 | H200 or B200 |
| 30B LLM Inference | L40S | 48 GB | FP8 | A100 80GB |
| 7B LLM Inference | RTX 4090 | 24 GB | FP16 | RTX 4090 |
| Fine-Tuning (LoRA) | A100 80GB | 80 GB | FP16 | H100 × 2 |
| Pre-Training | B200 | 192 GB | FP8 | B200 × 8 |
| Vision Model (72B) | H200 | 141 GB | FP8 | B200 |
Workload Suitability Profiles
Interconnect and RDMA availability for distributed workloads
Memory bandwidth and tensor-core throughput trade-offs
Storage volume costs and SLA guarantees for persistent APIs
Interruptible risk profile vs dedicated enterprise reliability
Spot pricing savings vs guaranteed uptime SLAs
Preemption risk and egress bandwidth charges per workload