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AMD Radeon RX 7900 XTX vs NVIDIA GeForce RTX 4090

Side-by-side comparison of AMD Radeon RX 7900 XTX (24 GB VRAM, 960 GB/s) and NVIDIA GeForce RTX 4090 (24 GB VRAM, 1.0 TB/s). Compare specs, compute throughput, and workload sizing for LLM inference.

Decision Summary

SpecAMD Radeon RX 7900 XTXNVIDIA GeForce RTX 4090
VRAM24GB GDDR624GB GDDR6X
Memory Bandwidth960 GB/s1.0 TB/s
FP8 TFLOPS—165
FP16 TFLOPS61.482.6
InterconnectPCIe 4.0 (64 GB/s)PCIe 4.0 (64 GB/s)
TDP355W450W
Recommended QuantizationGGUF / AWQ — INT4 mandatory for 13B+GGUF / AWQ / GPTQ — INT4 mandatory for 13B+

Compare for a Workload

Configure a workload to see how each selected GPU performs. Calculations are deterministic estimates based on architectural specifications.

Configure workload parameters above and click "Calculate VRAM Requirement" to see results.

Cloud Provider Pricing

Current spot and on-demand rates across providers for AMD Radeon RX 7900 XTX and NVIDIA GeForce RTX 4090.

Featured GPU Pricing Pages

NVIDIA H100 SXM5

80GB HBM3 • 3.35 TB/s • NVLink 4.0

NVIDIA A100 40GB

40GB HBM2 • 1.55 TB/s • NVLink 3.0

NVIDIA A100 80GB

Verified spot rates & reserved pricing

NVIDIA A10G

24GB GDDR6 • 0.62 TB/s • PCIe 4.0

NVIDIA RTX 3090

24GB GDDR6X • 0.94 TB/s • PCIe 4.0

NVIDIA T4

16GB GDDR6 • 0.32 TB/s • PCIe 3.0

ProviderGPU & VRAMInterconnectSpot Price ($/hr)On-Demand ($/hr)Monthly ($/720h)StatusAction
No providers match your filters.
On-demand and monthly figures are the providers' listed rates (monthly = listed rate, else hourly × 720h). Rows without a tracked rate show —. All rates subject to preemption and provider availability.
Data Freshness: Public Cloud APIs & Market Scraping | Refreshed Daily (UTC)Benchmark Baseline: Ubuntu 24.04, CUDA 12.4, vLLM v0.6.x, PagedAttention v2, FlashAttention-3

Prices verified daily from Spheron, RunPod, Vast.ai, and Lambda Labs APIs.

Microarchitecture & Interconnect

AMD Radeon RX 7900 XTX
ArchitectureRDNA 3
Process Node5nm TSMC
FP8 TFLOPS—
FP16 TFLOPS61.4
Compute BoundMemory-bandwidth bound at large batch; compute-bound at small batch with FP8/BF16
Recommended TopologyPCIe Single Node — consumer GPU
Recommended Quantization: GGUF / AWQ — INT4 mandatory for 13B+
NVIDIA GeForce RTX 4090
ArchitectureAda Lovelace AD102
Process Node5nm TSMC
FP8 TFLOPS165
FP16 TFLOPS82.6
Compute BoundExtremely memory-bandwidth bound — GDDR6X 1 TB/s cannot sustain FP16 inference at batch≥4
Recommended TopologyPCIe Single Node — 2 GPU max for training
Recommended Quantization: GGUF / AWQ / GPTQ — INT4 mandatory for 13B+

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Deploy AMD Radeon RX 7900 XTXDeploy NVIDIA GeForce RTX 4090
Data Freshness: Verified via Public Cloud APIs & Market Scraping | Refreshed Daily (UTC)Benchmark Baseline: Ubuntu 24.04, CUDA 12.4, vLLM v0.6.x (PagedAttention v2, FlashAttention-3), BF16/FP8 weights.
Methodology →