The RTX 3090 and RTX 4090 are the two most popular consumer GPUs for AI/ML work. Both have 24GB VRAM, but the price gap is massive. Let's break down when each one makes sense.
Specs Comparison
| Spec | RTX 3090 | RTX 4090 |
|---|---|---|
| Architecture | Ampere (CC 8.6) | Ada Lovelace (CC 8.9) |
| VRAM | 24 GB GDDR6X | 24 GB GDDR6X |
| Memory Bandwidth | 936 GB/s | 1,008 GB/s |
| CUDA Cores | 10,496 | 16,384 |
| Tensor Cores | 328 (3rd gen) | 512 (4th gen) |
| TDP | 350W | 450W |
| FP16 Tensor | 142 TFLOPS | 330 TFLOPS |
| New Price (2026) | Discontinued | ~$1,800 |
| Used Price (2026) | ~$600-700 | ~$1,400-1,500 |
For a detailed side-by-side with all specifications, see the RTX 4090 vs RTX 3090 comparison page on gpuark.com.
Training Performance
The 4090 is roughly 1.7-2× faster for training due to:
- 56% more CUDA cores
- 4th gen Tensor Cores (better FP8, BF16 throughput)
- Higher clock speeds
- Better power efficiency
Real-world training benchmarks:
| Task | RTX 3090 | RTX 4090 | Speedup |
|---|---|---|---|
| ResNet-50 (BS=64) | 780 img/s | 1,420 img/s | 1.82× |
| BERT fine-tune (BS=32) | 145 samples/s | 268 samples/s | 1.85× |
| Stable Diffusion training | 2.1 it/s | 3.8 it/s | 1.81× |
| LLaMA 7B LoRA (r=16) | 1.4 it/s | 2.6 it/s | 1.86× |
Inference Performance (LLMs)
For LLM inference, the gap narrows because it's memory-bandwidth bound:
| Task | RTX 3090 | RTX 4090 | Speedup |
|---|---|---|---|
| Llama 3.1 8B Q4 (tok/s) | 85 | 105 | 1.24× |
| Llama 3.1 70B Q4 (tok/s) | doesn't fit | doesn't fit | — |
| Mistral 7B Q4 (prompt) | 1,200 tok/s | 1,800 tok/s | 1.50× |
Memory bandwidth difference is only 8% (936 vs 1,008 GB/s), so for pure token generation the 4090 advantage is modest.
The Real Decision
Buy a 4090 if:
- Training throughput is your bottleneck (research, frequent fine-tuning)
- You need FP8 features (CC 8.9 vs 8.6)
- Power efficiency matters (performance per watt is much better)
- You want one powerful card, not multi-GPU hassle
Buy a used 3090 (or two) if:
- VRAM is your bottleneck (most LLM use cases)
- Budget matters — two 3090s = 48GB for ~$1,300 vs one 4090 = 24GB for ~$1,500
- You primarily do inference
- You want to run 34B+ models
The multi-GPU argument
Two used 3090s give you 48GB total VRAM for less than one 4090:
- Can run Llama 3.1 70B at Q4_K_M
- Pipeline parallelism with llama.cpp works out of the box
- Training with FSDP/DeepSpeed ZeRO-3 across both cards
The catch: inter-GPU communication over PCIe is slower than a single card's internal bandwidth. For training, expect ~1.5-1.7× scaling (not 2×). For inference with pipeline parallelism, the latency penalty is minimal.
Power Consumption
Often overlooked but significant:
| Config | TDP | Annual electricity (24/7) |
|---|---|---|
| 1× RTX 3090 | 350W | ~$370/year |
| 1× RTX 4090 | 450W | ~$475/year |
| 2× RTX 3090 | 700W | ~$740/year |
If running 24/7 as an inference server, the 4090's better perf/watt matters. For occasional use, it doesn't.
Bottom Line
The RTX 3090 at $600-700 used is the best value proposition in ML hardware right now. The 4090 is a better card in every metric except price-per-VRAM-GB, but the 3090 gives you 80% of the capability at 40% of the price.
If you're VRAM-limited (and you probably are if you're running LLMs), two 3090s beat one 4090 every time.
Running ML workloads on consumer GPUs? Share your setup in the comments!
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