Table of Contents
Overview: Two Different Approaches
The RTX 4060 and RTX 3090 represent two very different strategies for dedicated GPU hosting. The 4060 is newer (Ada Lovelace) with better power efficiency, but has only 8GB VRAM. The 3090 is older (Ampere) but packs 24GB VRAM — three times more memory for model loading.
We tested both on our UK-based servers across LLM inference, image generation, and speech workloads. The results reveal that architecture generation matters less than raw VRAM for most AI tasks. For the full GPU lineup, visit our GPU comparisons hub.
Specs Comparison
| Spec | RTX 4060 | RTX 3090 |
|---|---|---|
| Architecture | Ada Lovelace | Ampere |
| VRAM | 8 GB GDDR6 | 24 GB GDDR6X |
| Memory Bandwidth | 272 GB/s | 936 GB/s |
| CUDA Cores | 3,072 | 10,496 |
| TDP | 115W | 350W |
| FP16 Throughput | ~24 TFLOPS | ~71 TFLOPS |
The RTX 3090 has 3.4x more CUDA cores, 3.4x more memory bandwidth, and 3x more VRAM. On paper, it’s a completely different tier.
LLM Inference: Where VRAM Wins
For open source LLM inference, VRAM determines which models you can run. Using vLLM:
| Model | RTX 4060 (tok/s) | RTX 3090 (tok/s) | Notes |
|---|---|---|---|
| LLaMA 3 8B (GPTQ 4-bit) | 22 | 28 | Both run fine |
| Mistral 7B (GPTQ 4-bit) | 24 | 30 | Both run fine |
| LLaMA 3 8B (FP16) | Cannot load | 42 | 8GB not enough |
| LLaMA 3 13B (GPTQ 4-bit) | Cannot load | 28 | 8GB not enough |
The RTX 4060 can only run 7B models in 4-bit quantization. The RTX 3090 runs 7B at full precision AND 13B quantized. For production LLM hosting, 24GB VRAM gives you far more flexibility. See our best GPU for LLM inference guide for the full benchmark.
Image Generation Performance
For AI image generation, the gap is smaller on models that fit in 8GB:
| Model | RTX 4060 (it/s) | RTX 3090 (it/s) |
|---|---|---|
| SD 1.5 512×512 | 8.2 | 12.5 |
| SDXL 1024×1024 | 1.8 | 3.2 |
| Flux.1 512×512 | Cannot load | 2.1 |
Flux.1 requires more than 8GB VRAM. If you’re hosting image generation at scale, the RTX 3090 is the minimum viable option. For dedicated Stable Diffusion setups, see our best GPU for Stable Diffusion guide.
Speech & Audio Workloads
Whisper and TTS models are typically small enough for 8GB. This is where the RTX 4060 actually competes. For details, check our TTS latency benchmarks.
If speech is your primary workload, the RTX 4060 on a speech model hosting setup is a cost-effective choice. For everything else, the 3090 wins.
Compare Pricing Live
See real-time pricing for RTX 4060 and RTX 3090 dedicated servers with full root access.
Browse GPU ServersWhich Should You Pick?
Choose the RTX 4060 if:
- Your workload fits in 8GB VRAM (small quantized LLMs, speech-to-text, TTS)
- Budget is your primary constraint
- Power efficiency matters (115W vs 350W TDP)
Choose the RTX 3090 if:
- You need to run models larger than 7B quantized
- You want FP16 inference quality
- You’re building a production API that needs headroom
- Image generation is part of your workload
For a complete cost comparison of self-hosting vs. API services, use our GPU vs API cost calculator. Both GPUs are available on our dedicated GPU hosting platform.
