NVIDIA GeForce RTX 4090 vs NVIDIA GeForce RTX 3090
Spec-driven comparison from our catalog. For curated editorial verdicts on the most-asked pairs, see the head-to-head index.
Editorial verdict available: We have a hand-written buyer guide for this exact pair. Read the editorial verdict →
Pick your two cards
Spec matrix
| Dimension | NVIDIA GeForce RTX 4090 | NVIDIA GeForce RTX 3090 |
|---|---|---|
| VRAM | 24 GB high (70B Q4 comfortable) | 24 GB high (70B Q4 comfortable) |
| Memory bandwidth | 1008 GB/s strong (800 GB/s - 1.5 TB/s) | — — |
| FP16 compute | 82.6 TFLOPS | — |
| FP8 compute | — | — |
| Power draw | 450 W extreme (1000W+ PSU) | 350 W enthusiast (850W PSU) |
| Price | ~$1,899 (street) | ~$899 (street) |
| Release year | 2022 | 2020 |
| Vendor | nvidia | nvidia |
| Runtime support | CUDA, Vulkan | CUDA, Vulkan |
Spec data from our hardware catalog. This is a generated spec compare, not a hand-written editorial verdict. For editorial picks on the most-asked pairs, see our curated head-to-heads.
Most users should buy
NVIDIA GeForce RTX 3090
Same VRAM tier (24 GB vs 24 GB) but the NVIDIA GeForce RTX 3090 is dramatically cheaper. The NVIDIA GeForce RTX 4090's premium isn't justified for VRAM-bound workloads at this tier.
Decision rules
No strong differentiators in NVIDIA GeForce RTX 4090's favor at this comparison tier.
- You're cost-conscious — saves ~$1,000 vs the NVIDIA GeForce RTX 4090.
Biggest buyer mistake on this comparison
Buying based on the spec sheet without verifying the actual workload requirement. Run /will-it-run with your specific model + context-length combination before committing — the math is exact and frequently surprising.
Workload fit
How each card handles common local AI workloads. “Tie” means both cards meet the bar; pick on other axes (price, ecosystem, form factor).
| Workload | Winner | Notes |
|---|---|---|
| Coding agents (Aider, Cursor, Continue) | Tie | Code agents work fine on 16 GB for 13-32B models. 24 GB unlocks 70B-class code models (DeepSeek Coder V3, Qwen 2.5 Coder). |
| Ollama / LM Studio chat | Tie | Both run Ollama fine. 16 GB unlocks multi-model serving via OLLAMA_KEEP_ALIVE. |
| Image generation (SDXL, Flux Dev) | NVIDIA GeForce RTX 4090 | Image gen is compute-bound. 24 GB VRAM unlocks Flux Dev FP16 + LoRA training. Below 24 GB, Flux Dev FP8 only with offloading. |
| Local RAG (embedding + LLM) | Tie | RAG with 70B LLM concurrent fits at 24 GB. Embedding model overhead is negligible (<1 GB). |
| Long-context chat (32K+ context) | Tie | 24 GB fits 70B Q4 at 8-16K context. KV cache quantization (Q8 cache) extends to 32K with care. |
| Voice / Whisper transcription | Tie | Whisper Large V3 fits in 4-8 GB. Both cards likely overkill for transcription-only workloads. |
| Video generation (LTX-Video, Mochi) | Tie | Local video gen viable at 24 GB. Plan for short clips, not long-form. |
| Multi-GPU tensor parallel (vLLM, ExLlamaV2) | Tie | Tensor-parallel scaling works on PCIe 4.0 x8/x16. Used cards typically win on $/GB-VRAM at scale (dual 3090 vs single 5090). |
VRAM reality check
- Multi-GPU does NOT pool VRAM by default. Two 24 GB cards = 48 GB combined ONLY when the runtime supports tensor-parallel inference (vLLM, ExLlamaV2, llama.cpp split-mode). For models that don't tensor-parallel cleanly, you're stuck at single-card VRAM.
- At 24 GB, 70B Q4 fits with 4-8K context comfortably. FP16 32B fits. 32K+ context on 70B Q4 starts to get tight — KV cache quantization (Q8 cache) extends this another ~30%.
Power, noise, and thermals
- NVIDIA GeForce RTX 4090 TDP: 450W. NVIDIA GeForce RTX 3090 TDP: 350W. Plan PSU sizing for transient spikes — sustained AI inference draws closer to nameplate TDP than gaming benchmarks suggest. Add 200-250W headroom over GPU TDP for the rest of the system.
- Used cards: replace thermal pads on any used purchase older than 18 months ($30-50 + 1 hour of work). Ex-mining cards specifically — cooler reseat improves thermals 5-10°C, often the difference between throttling and stable load.
Used-market intelligence
- Mining-rig provenance is dominant for used NVIDIA GeForce RTX 4090 listings. Not inherently disqualifying — mining wears fans (replaceable) and thermal pads (replaceable), rarely silicon. Verify ECC error counts with nvidia-smi (or vendor equivalent); any value above ~100 = walk away.
- Demand a 30-minute under-load demonstration before paying — screen-recorded inference at 90%+ utilization. Sellers refusing this are red flags.
- Replace thermal pads on any used GPU older than 18 months. Cheap insurance ($30-50 + 1 hour) that often delivers 5-10°C cooler operation under sustained inference.
- Used cards have no warranty. Budget for a 2-3 year operational horizon and plan to resell if your usage tier changes. Used silicon resale is mature in 2026 — selling later is realistic.
Upgrade-path logic
- If you already own the NVIDIA GeForce RTX 4090, the NVIDIA GeForce RTX 3090 is a side-grade — same VRAM tier means same workload ceiling. Only upgrade if you specifically need newer architecture features (FP8 native, FlashAttention 3, warranty refresh).
Better alternatives to consider
Quick takes
NVIDIA GeForce RTX 4090
The community-default high-end local-AI card from 2022 to 2025. 24GB GDDR6X at ~1 TB/s makes 70B Q4 comfortably loadable.
Full verdict →NVIDIA GeForce RTX 3090
The original 24GB CUDA value pick. Used market still strong in 2026 — many AI hobbyists run dual 3090 setups for 70B inference.
Full verdict →