NVIDIA RTX 3090 vs RTX 4090 for Local AI & LLM Inference
Exact hardware breakdown, VRAM sizing, tokens/second inference throughput, and truthful total cost-of-ownership math.
AI Architect Verdict
RTX 3090 Wins on Value-per-Dollar; RTX 4090 Wins on Raw Speed & Efficiency
Both cards have identical 24GB VRAM capacity, meaning they can run the exact same maximum model sizes (e.g. Qwen 2.5 Coder 32B at Q4 or LLaMA 3.1 8B at FP16). The RTX 4090 delivers ~25% higher token generation throughput (42 vs 34 tok/s) and supports FP8 KV-cache natively, but costs over 2.5x more. For homelabs on a budget, buying two used RTX 3090s ($1,400 total) provides 48GB VRAM—unlocking 70B models that a single RTX 4090 physically cannot run.
NVIDIA GeForce RTX 3090 (24GB)
VRAM: 24 GB GDDR6X
Memory Bus: 384-bit (936 GB/s)
Typical Price: $650 – $750 (Used)
Inference Speed: ~34 tok/s (32B Q4)
NVIDIA GeForce RTX 4090 (24GB)
VRAM: 24 GB GDDR6X
Memory Bus: 384-bit (1,008 GB/s)
Typical Price: $1,700 – $1,900 (New)
Inference Speed: ~42 tok/s (32B Q4)
Popular AI Model Fit Comparison
| AI Model | VRAM Req (8k) | NVIDIA GeForce RTX 3090 | NVIDIA GeForce RTX 4090 |
|---|---|---|---|
| Qwen 2.5 Coder 32B (Q4_K_M) | 21.6 GB | Runs Comfortably (+2.4GB) | Runs Comfortably (+2.4GB) |
| Meta LLaMA 3.3 70B (Q4_K_M) | 44.1 GB | Exceeds VRAM (OOM) | Exceeds VRAM (OOM) |
| Meta LLaMA 3.1 8B (FP16) | 19.4 GB | Runs Comfortably (+4.6GB) | Runs Comfortably (+4.6GB) |
| FLUX.1 [dev] NF4 / 4-bit | 12.0 GB | Runs Comfortably (+12.0GB) | Runs Comfortably (+12.0GB) |
Calculate Custom Context Windows & Precision
Inspect exact KV-cache formulas, test 32k/128k context allocations, or compare cloud rental break-even costs in the interactive matchmaker.
Launch Interactive Showdown Calculator →