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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 6000 Ada

wins

1

The RTX 6000 Ada is NVIDIA's professional workstation GPU with 48GB GDDR6 and 364 TFLOPS FP16.

Professional inferenceFine-tuningWorkstation AI

Performance comparison

Visual bars — winner highlighted

AMD Instinct MI355X

Metric

NVIDIA RTX 6000 Ada

FP16 TFLOPS

2.6k T
364 T

Bandwidth

8.0k GB/s
960 GB/s

VRAM

2.9k GB
486 GB

TDP (lower=better)

750 W
300 W

TFLOPS/watt

3.49
1.21

GB/s per watt

10.67
3.20

Full specification table

SpecAMD Instinct MI355XNVIDIA RTX 6000 Ada
ArchitectureCDNA 4Ada Lovelace
VRAM288 GB HBM3e48 GB GDDR6
VRAM typeHBM3eGDDR6
Memory bandwidth8,000 GB/s960 GB/s
FP16 TFLOPS2,615 TFLOPS364 TFLOPS
TDP750 W300 W
TFLOPS/watt3.49 T/W1.21 T/W
GB/s per watt10.67 GB/s/W3.20 GB/s/W
NVLinkNoNo
Release year20252022

Live cloud pricing

On-demand pricing across providers — updated in real time

Per hourPer GB VRAM/hr

AMD Instinct MI355X

No live pricing data available.

NVIDIA RTX 6000 Ada

QuantaCloud
$0.78/hr

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 6000 Ada

FP16 TFLOPS364 TFLOPS
TDP300 W
TFLOPS/watt1.213 T/W
GB/s per watt3.20 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.

Performance-per-dollar

TFLOPS/$ and GB/s/$ at current best cloud prices

AMD Instinct MI355X

Best cloud priceN/A
TFLOPS per $—
GB/s per $—

NVIDIA RTX 6000 Ada

Best cloud price$0.78/hr
TFLOPS per $468.9 T/$
GB/s per $1237 GB/s/$

TFLOPS/$ = FP16 TFLOPS ÷ best on-demand $/hr. Higher is better — more compute per dollar. GB/s/$ = memory bandwidth ÷ best on-demand $/hr. For memory-bound inference workloads (most LLM serving), GB/s/$ is often a better predictor of cost-efficiency than TFLOPS/$. Prices are live from the Compute Comparison API and update in real time.

Workload Cost Comparison

Total job cost = best on-demand $/hr × GPU count × hours

WorkloadScaleAMD Instinct MI355XNVIDIA RTX 6000 Ada
7B LLM fine-tune8× GPU, 24 h—$149
70B LLM fine-tune64× GPU, 72 h—$3.6k
Inference server1× GPU, 730 h—$567
Image gen batch4× GPU, 8 h—$25

Based on current best on-demand prices. Spot pricing typically 40–70% lower. Actual cost depends on provider, region, and availability.

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 6000 Ada for:

  • Professional inference
  • Fine-tuning
  • Workstation AI

Weaknesses / Limitations

  • GDDR6 bandwidth lower than HBM-based data center GPUs
  • No NVLink on Ada generation
  • Workstation positioning means higher list price than L40S for similar specs