NVIDIA RTX 3060 12GB vs NVIDIA GeForce RTX 4090

Side-by-side comparison of NVIDIA RTX 3060 12GB (12 GB VRAM, 360 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

Spec	NVIDIA RTX 3060 12GB	NVIDIA GeForce RTX 4090
VRAM	12GB GDDR6	24GB GDDR6X
Memory Bandwidth	360 GB/s	1.0 TB/s
FP8 TFLOPS	—	165
FP16 TFLOPS	26.4	82.6
Interconnect	PCIe 4.0 (64 GB/s)	PCIe 4.0 (64 GB/s)
TDP	170W	450W
Recommended Quantization	GGUF / AWQ — INT4 mandatory	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.

Workload

Context Length

Precision

Batch Size

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

Cloud Provider Pricing

Current spot and on-demand rates across providers for NVIDIA RTX 3060 12GB 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

	Provider	GPU & VRAM	Interconnect	Spot Price ($/hr)	On-Demand ($/hr)	Monthly ($/720h)	Status	Action
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

NVIDIA RTX 3060 12GB

ArchitectureAmpere GA102

Process Node8nm Samsung

FP8 TFLOPS—

FP16 TFLOPS26.4

Compute BoundMemory-bound — GDDR6 bus cannot feed compute units even at modest batch sizes

Recommended TopologyPCIe Single Node — budget edge

Recommended Quantization: GGUF / AWQ — INT4 mandatory

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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48GB GDDR6 vs 12GB GDDR6

NVIDIA A100 80GB SXM4 vs NVIDIA GeForce RTX 4090→

80GB HBM2e vs 24GB GDDR6X

NVIDIA L40S vs NVIDIA GeForce RTX 4090→

48GB GDDR6 vs 24GB GDDR6X

NVIDIA H100 SXM5 vs NVIDIA GeForce RTX 4090→

80GB HBM3 vs 24GB GDDR6X

Deploy NVIDIA RTX 3060 12GB Deploy 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 →