Hopper
NVIDIA H100 PCIe 80GB
wins
4
The H100 PCIe is the more affordable form factor of the H100.
Ada Lovelace
NVIDIA RTX 4060 8GB
wins
1
The RTX 4060 is the most power-efficient Ada Lovelace GPU at 115W TDP. 8GB GDDR6 limits it to small quantized models (up to ~7B at INT4).
Performance comparison
Visual bars — winner highlighted
NVIDIA H100 PCIe 80GB
Metric
NVIDIA RTX 4060 8GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | NVIDIA H100 PCIe 80GB | NVIDIA RTX 4060 8GB |
|---|---|---|
| Architecture | Hopper | Ada Lovelace |
| VRAM | 80 GB HBM2e | 8 GB GDDR6 |
| VRAM type | HBM2e | GDDR6 |
| Memory bandwidth | 2,000 GB/s | 272 GB/s |
| FP16 TFLOPS | 756 TFLOPS | 30.2 TFLOPS |
| TDP | 350 W | 115 W |
| TFLOPS/watt | 2.16 T/W | 0.26 T/W |
| GB/s per watt | 5.71 GB/s/W | 2.37 GB/s/W |
| NVLink | No | No |
| Release year | 2022 | 2023 |
Live cloud pricing
On-demand pricing across providers — updated in real time
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
NVIDIA H100 PCIe 80GB
NVIDIA RTX 4060 8GB
TFLOPS/watt measures compute efficiency — how much AI throughput you get per watt of power consumed. For data centers with PUE of 1.2–1.5, a 10% improvement in TFLOPS/watt translates directly to lower electricity costs and cooling requirements. GB/s/watt measures memory bandwidth efficiency, which is the binding constraint for memory-bound LLM inference workloads.
When to choose each GPU
Choose NVIDIA H100 PCIe 80GB for:
- Budget Hopper inference
- PCIe server deployments
- Cost-sensitive LLM serving
Weaknesses / Limitations
- Lower FP16 TFLOPS (756) and bandwidth (2,000 GB/s) than H100 SXM5
- No NVLink — cannot scale across cards for very large models
- Higher cost than A100 with only moderate performance advantage
Choose NVIDIA RTX 4060 8GB for:
- Ultra-budget inference
- Small model experimentation
- Entry-level Ada AI
Weaknesses / Limitations
- 8 GB VRAM — only fits quantized models up to ~7B parameters
- Very low bandwidth (272 GB/s) — severe bottleneck for inference throughput
- Not viable for fine-tuning beyond tiny models
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