Compute Comparison
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CDNA 4

AMD Instinct MI355X

wins

4

The AMD Instinct MI355X is AMD's CDNA 4 flagship.

Large LLM inferenceAgent workloadsMemory-bound AI

Ada Lovelace

NVIDIA RTX 4060 Ti 16GB

wins

1

The RTX 4060 Ti 16GB doubles the VRAM of the RTX 4060 at 16GB GDDR6, enabling 7B–13B model inference at INT4/INT8.

Budget inferenceSmall model fine-tuningEntry Ada AI

Performance comparison

Visual bars — winner highlighted

AMD Instinct MI355X

Metric

NVIDIA RTX 4060 Ti 16GB

FP16 TFLOPS

2.6k T
44 T

Bandwidth

8.0k GB/s
288 GB/s

VRAM

2.9k GB
166 GB

TDP (lower=better)

750 W
165 W

TFLOPS/watt

3.49
0.27

GB/s per watt

10.67
1.75

Full specification table

SpecAMD Instinct MI355XNVIDIA RTX 4060 Ti 16GB
ArchitectureCDNA 4Ada Lovelace
VRAM288 GB HBM3e16 GB GDDR6
VRAM typeHBM3eGDDR6
Memory bandwidth8,000 GB/s288 GB/s
FP16 TFLOPS2,615 TFLOPS44.2 TFLOPS
TDP750 W165 W
TFLOPS/watt3.49 T/W0.27 T/W
GB/s per watt10.67 GB/s/W1.75 GB/s/W
NVLinkNoNo
Release year20252023

Live cloud pricing

On-demand pricing across providers — updated in real time

Per hourPer GB VRAM/hr
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Power efficiency analysis

TFLOPS/watt and GB/s/watt — critical for data center TCO

AMD Instinct MI355X

FP16 TFLOPS2,615 TFLOPS
TDP750 W
TFLOPS/watt3.487 T/W
GB/s per watt10.67 GB/s/W

NVIDIA RTX 4060 Ti 16GB

FP16 TFLOPS44.2 TFLOPS
TDP165 W
TFLOPS/watt0.268 T/W
GB/s per watt1.75 GB/s/W

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 AMD Instinct MI355X for:

  • Large LLM inference
  • Agent workloads
  • Memory-bound AI

Weaknesses / Limitations

  • ROCm ecosystem less mature than CUDA — some frameworks require porting
  • Very limited cloud availability compared to NVIDIA equivalents
  • 750W TDP — high power draw comparable to H100 SXM

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