The RTX 5090 and RTX 4090 represent two generations of NVIDIA's best consumer GPUs. If you're deciding whether to upgrade — or build a new AI workstation — this guide gives you the honest numbers.
Quick Summary
| RTX 4090 | RTX 5090 | |
|---|---|---|
| VRAM | 24 GB GDDR6X | 32 GB GDDR7 |
| Memory Bandwidth | 1,008 GB/s | 1,792 GB/s |
| TDP | 450W | 575W |
| Launch MSRP | $1,599 | $1,999 |
| Llama 3.1 8B Q8 | 165 t/s | 213 t/s |
| Qwen 3 32B Q4 | 45 t/s | 61 t/s |
| Max single-card model | ~24B (Q4) | ~32B (Q4) |
Why the RTX 5090 Wins on Paper
The RTX 5090 uses GDDR7 memory with 1,792 GB/s bandwidth — 78% more than the RTX 4090's GDDR6X at 1,008 GB/s. For LLM inference, memory bandwidth is the primary bottleneck, not compute. This explains why tokens-per-second scales nearly linearly with bandwidth.
Benchmark results (Q4_K_M, Ollama, Windows 11):
- Llama 3.1 8B: RTX 5090 = 213 t/s vs RTX 4090 = 165 t/s (+29%)
- Qwen 3 14B: RTX 5090 = 148 t/s vs RTX 4090 = 98 t/s (+51%)
- Llama 4 Scout: RTX 5090 = 175 t/s vs RTX 4090 = 92 t/s (+90%)
- Qwen 3 32B: RTX 5090 = 61 t/s vs RTX 4090 = 45 t/s (+36%)
The VRAM Advantage: 32 GB Changes What's Possible
On Windows, going over the card's VRAM does not produce an error — the driver spills into shared GPU memory and the model crawls instead of crashing. On a laptop, check first that the discrete GPU is the one being used at all.
The RTX 4090's 24 GB VRAM cannot fit a 32B model comfortably — you need aggressive quantization (IQ2-IQ3) that degrades quality. The RTX 5090's 32 GB VRAM fits 32B models in Q4_K_M with 4+ GB to spare for context.
| Model | RTX 4090 | RTX 5090 |
|---|---|---|
| Llama 3.1 8B Q8 | ✅ Fits | ✅ Fits |
| Qwen 3 14B Q4 | ✅ Fits | ✅ Fits |
| DeepSeek R1 32B Q4 | ⚠️ Tight (22GB, 2GB left) | ✅ Comfortable |
| Llama 4 Maverick | ❌ Too large | ⚠️ Partial |
| Llama 3.3 70B Q4 | ❌ Too large (42GB needed) | ❌ Too large |
Power & Noise
The RTX 5090 draws 575W at peak — 125W more than the RTX 4090. At 8 hours/day, that's an extra $5–8/month in electricity (US average). Noise is comparable — both require large triple-fan coolers.
Should You Upgrade from RTX 4090?
Upgrade if:
- You regularly run 20–32B models and feel VRAM pressure
- Speed matters to you (you'll notice the 29–90% improvement)
- You're building a new system and can budget $1,999
Don't upgrade if:
- Your workload is 7–14B models (the 4090 is already fast enough)
- You bought the 4090 recently (the performance gain doesn't justify the cost differential)
- Power consumption is a concern
The RTX 3090 Alternative
If you're coming from a mid-range card, consider the RTX 3090 (24 GB, available used for $400–600). It offers the same 24 GB VRAM as the RTX 4090 at a fraction of the cost, at ~70% of the inference speed.
Verdict
The RTX 5090 is the best single consumer GPU for local AI in 2026. But unless you specifically need 32 GB VRAM or maximum throughput, the RTX 4090 (or RX 7900 XTX for AMD users) remains a more cost-efficient choice for 7–14B model workloads.
Own one of these cards? A run measured the same way as everyone else's is worth more than any figure above: benchmark your setup reproducibly and submit it.
To size a specific model, use the VRAM calculator: the memory it needs at every quantization and context length.