Max headroom and speed
GeForce RTX 5090
VRAM
32 GB GDDR7
Bandwidth
1,792 GB/s
Power
575 W
Typical Price
$1,999.99
Apr 12, 2026
GeForce RTX 5090 vs GeForce RTX 4090 for Local LLMs: When Paying $800 More Is Justified
This comparison is not about gaming FPS. It is about model-fit headroom, memory bandwidth, and whether your workload lives in the 24 GB tier or the 32 GB tier.
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1.0
Quick Verdict
Choose the RTX 5090 if you run 70B-class models frequently, need larger context windows, and want fewer offload compromises in a single-GPU setup. The RTX 5090 ships with 32 GB GDDR7 and 1,792 GB/s bandwidth — the highest of any consumer GPU.
Choose a used RTX 4090 if your models fit in 24 GB and you care more about value than absolute peak throughput. It remains one of the strongest price-to-performance cards for local LLMs on Ollama and llama.cpp. See our full GPU rankings for context on where both cards sit in the broader market. Use our VRAM Calculator to verify your target model fits in 24 GB or whether you need 32 GB.
2.0
At a Glance
Price
Best high-end value
GeForce RTX 4090
VRAM
24 GB GDDR6X
Bandwidth
1,008 GB/s
Power
450 W
Typical Price
$1,599.99
3.0
Spec by Spec
| Specification | GeForce RTX 5090 | GeForce RTX 4090 |
|---|---|---|
| VRAM | 32 GB GDDR7 | 24 GB GDDR6X |
| Bandwidth | 1,792 GB/s | 1,008 GB/s |
| Architecture | Blackwell | Ada Lovelace |
| Street Price | $1,999 new | ~$1,200 used |
| FP8 Path | Yes | Yes |
| Board Power | 575 W | 450 W |
| Recommended PSU | 1,000 W | 850 W |
| Warranty Position | Full retail warranty | Varies by seller |
| Max Practical Single-GPU Tier | 70B class with fewer compromises | 35B class comfortably |
4.0
Model Fit and Throughput Reality
The 8 GB VRAM difference is the key divider. Both cards are very fast, but the 5090 shifts large-model work from heavy offload toward viable single-card operation.
| Workload | GeForce RTX 5090 | GeForce RTX 4090 | Practical Outcome |
|---|---|---|---|
| 7B to 13B models | Excellent | Excellent | Both are fast enough |
| 32B to 35B Q4 | Comfortable | Comfortable | Both good, 5090 faster |
| 70B Q4 | Closer to practical | Heavy offload | 5090 has clear edge |
| Long-context runs | More headroom | More constrained | 5090 scales better |
| Power and cooling | Higher demands | Lower demands | 4090 easier to tame |
5.0
Who Should Buy Which
Buy RTX 5090 If
- -You actively use 70B-class models and want less offloading.
- -You value highest throughput and future VRAM headroom.
- -You can support the power, thermal, and budget requirements.
Buy Used RTX 4090 If
- -Your workload stays mostly inside the 24 GB model-fit tier.
- -You want strong CUDA performance at materially lower cost.
- -You accept used-market buying risk in exchange for value.
6.0
The Bottom Line
For 24 GB-class workloads, the used GeForce RTX 4090 remains the better value decision. The CUDA toolkit ecosystem is the same on both cards, so the question is purely VRAM versus budget.
For users who are bottlenecked by VRAM headroom and run larger models frequently, the GeForce RTX 5090 is the better long-term workstation purchase.
7.0
Related Comparisons
24 GB vs 32 GB GPU for Local LLMs
The deeper dive on whether the VRAM step-up justifies the cost.
RTX 5080 vs Used RTX 4090
16 GB new vs 24 GB used — the midrange version of this same decision.
Best NVIDIA GPU for Local LLMs
Every NVIDIA card ranked, from RTX 5090 to RTX 4070 Ti Super.
Best 24 GB GPU for Local LLMs
RTX 4090 vs RX 7900 XTX vs RTX 3090 at the 24 GB tier.
FAQ
Frequently Asked Questions
Is the RTX 5090 worth the premium over a used RTX 4090 for LLMs?
Only if you need 32 GB VRAM regularly. For workloads that fit in 24 GB, a used 4090 is usually better value.
Does the RTX 5090 generate tokens faster than the RTX 4090?
Yes. The 5090 has much higher memory bandwidth and will usually deliver materially better throughput when both cards can hold the same model.
Can the RTX 4090 run Llama 70B?
Not comfortably at Q4 in a single-card configuration. It usually requires significant offloading. The 5090 still may offload for large contexts but is much closer to practical single-card use.
Is FP8 support different between the two?
Both support FP8-class workflows. For most local inference users, VRAM and bandwidth matter more than FP8 differences between these two.
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