Efficiency Frontier
Visualize the price-performance landscape. Compare raw throughput (TFLOPS) or inference speed (Tokens/Sec) against Market Price or Manufacturing Cost.
Simulates performance when connecting multiple chips together. Observe how scaling penalty affects efficiency.
1 Chip
Volume Discount
0%Discount rate for bulk purchase. 10-20% is common for 100+ units.
18645124.096k16.384k
Metric Basis
Switch between what you pay (Market Price) vs. what it costs to make (Manufacturing Cost).
Inference Workload
Select a specific AI model to see how fast it runs (Tokens/Sec).
Electricity Cost
$0.15/kWhCost per kilowatt-hour.
PUE (Cooling)
1.20xPower Usage Effectiveness. Multiplier for cooling overhead. 1.5 = 50% extra energy for cooling.
Performance King
Nvidia Blackwell B200
Highest raw throughput
Bandwidth King
Nvidia Blackwell B200
Highest memory bandwidth
Value King
Intel Gaudi 3
117 GFLOPS/$
Efficiency King
Nvidia Blackwell B100
Lowest Watts per TFLOP
Nvidia (Green)
AMD (Red)
Intel (Blue)
Google/AWS (Custom)
Best Value Configs (Top 5)
| Rank | Chip / Cluster | Raw Value (Perf/$1M) | Ecosystem Maturity | Strategic Verdict |
|---|---|---|---|---|
| #1 | Intel Gaudi 3 Ethernet (RoCE) | 117,440 | OneAPI (Specific) | High Engineering Overhead |
| #2 | AMD Instinct MI300X Infinity Fabric | 87,133 | ROCm (Maturing) | High Engineering Overhead |
| #3 | AMD Instinct MI325X Infinity Fabric | 65,350 | ROCm (Maturing) | High Engineering Overhead |
| #4 | Nvidia Blackwell B100 NVLink | 56,250 | CUDA (Dominant) | Low Friction |
| #5 | Nvidia Blackwell B200 NVLink | 56,250 | CUDA (Dominant) | Low Friction |
The "Value Trap": Why isn't the cheapest chip the winner?
While AMD and Intel often win on "Paper Value" (Raw TFLOPS per Dollar), Nvidia retains 80%+ market share due to the "Software Moat."
- Engineering Time: Saving $5k on hardware is lost if your $200k/yr engineers spend 3 months porting code from CUDA to ROCm.
- Reliability at Scale: At 10,000+ GPUs, Nvidia's mature drivers often crash less frequently than competitors, saving millions in idle cluster time.
Hyperscaler Reality: Trainium & TPU
AWS Trainium and Google TPU often appear lower on "Raw Specs" charts. This is misleading. Their value comes from Vertical Integration.
- Zero Margin Stacking: Google/AWS pay "Manufacturing Cost," not "Market Price." They effectively get a ~50-70% discount vs. buying Nvidia.
- System-Level Yield: They don't need "Hero Specs" (Peak TFLOPS). They optimize for stable, sustained throughput across 50,000 chips using custom liquid cooling and optical fabrics.