Ampere
NVIDIA A10G
wins
5
The NVIDIA A10G is the AWS-specific variant of the A10, available on g5 instances.
Ada Lovelace
NVIDIA RTX 4060 Ti 16GB
wins
0
The RTX 4060 Ti 16GB doubles the VRAM of the RTX 4060 at 16GB GDDR6, enabling 7B–13B model inference at INT4/INT8.
Performance comparison
Visual bars — winner highlighted
NVIDIA A10G
Metric
NVIDIA RTX 4060 Ti 16GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | NVIDIA A10G | NVIDIA RTX 4060 Ti 16GB |
|---|---|---|
| Architecture | Ampere | Ada Lovelace |
| VRAM | 24 GB GDDR6 | 16 GB GDDR6 |
| VRAM type | GDDR6 | GDDR6 |
| Memory bandwidth | 600 GB/s | 288 GB/s |
| FP16 TFLOPS | 125 TFLOPS | 44.2 TFLOPS |
| TDP | 150 W | 165 W |
| TFLOPS/watt | 0.83 T/W | 0.27 T/W |
| GB/s per watt | 4.00 GB/s/W | 1.75 GB/s/W |
| NVLink | No | No |
| Release year | 2021 | 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 A10G
NVIDIA RTX 4060 Ti 16GB
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 A10G for:
- AWS inference
- Graphics workloads
- Multi-tenant AI
Weaknesses / Limitations
- AWS-specific — not available on other cloud platforms
- 24 GB VRAM ceiling limits model size
- Older Ampere architecture — lower TFLOPS than Ada equivalents
Choose NVIDIA RTX 4060 Ti 16GB for:
- Budget inference
- Small model fine-tuning
- Entry Ada AI
Weaknesses / Limitations
- Low bandwidth (288 GB/s) — inference throughput bottleneck
- 16 GB VRAM ceiling — cannot run 34B+ models
- Consumer GPU — no ECC memory or enterprise support
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