Table of Contents
Specs Overview: RTX 3090 vs RTX 5080
Choosing the right dedicated GPU server for inference starts with understanding the hardware. The RTX 3090 launched as NVIDIA’s Ampere flagship with 24 GB GDDR6X and 936 GB/s memory bandwidth. The RTX 5080, built on the Blackwell architecture, brings 16 GB GDDR7 with improved bandwidth efficiency and newer tensor cores.
The 3090 retains a significant VRAM advantage at 24 GB versus 16 GB, which matters for larger quantised models. However, the 5080’s architectural improvements deliver better performance per CUDA core. For a broader look at GPU matchups, see our GPU comparisons category.
Throughput Benchmarks Across Model Sizes
We benchmarked both GPUs using vLLM with common quantised models to measure real-world tokens per second output.
| Model | Quantisation | RTX 3090 (tok/s) | RTX 5080 (tok/s) | Difference |
|---|---|---|---|---|
| Llama 3 8B | GPTQ 4-bit | 92 | 105 | +14% |
| Mistral 7B | AWQ 4-bit | 98 | 112 | +14% |
| Llama 3 13B | GPTQ 4-bit | 58 | 64 | +10% |
| Mixtral 8x7B | GPTQ 4-bit | 35 | N/A (16 GB VRAM) | — |
| Llama 3 70B | AWQ 4-bit | N/A (needs multi-GPU) | N/A | — |
For models up to 13B parameters, the 5080 leads by 10-14%. However, larger MoE models like Mixtral only fit on the 3090’s 24 GB. Check our tokens per second benchmark tool for live comparisons.
Monthly Cost and Throughput per Dollar
Raw speed means nothing without factoring in cost. Here is how throughput per dollar compares on a dedicated GPU hosting plan.
| Metric | RTX 3090 | RTX 5080 |
|---|---|---|
| Approx. monthly cost | ~$140/mo | ~$195/mo |
| Llama 3 8B tok/s | 92 | 105 |
| tok/s per $/mo | 0.657 | 0.538 |
| Cost per 1M tokens | $0.058 | $0.071 |
The RTX 3090 delivers roughly 22% more throughput per dollar despite being the older card. Use our cost per million tokens calculator to model your own workload economics.
VRAM Capacity and Workload Fit
VRAM determines which models you can serve. The 3090’s 24 GB handles most 13B 4-bit models comfortably, with room for KV cache at reasonable batch sizes. The 5080 at 16 GB fits 7-8B models with generous KV cache headroom, but 13B models run tight.
If your production workload targets open-source LLMs in the 7B range, the 5080 works well. For teams needing 13B+ models on a single card, the RTX 3090 remains the practical choice. For guidance on memory planning, read our vLLM memory optimisation guide.
Break-Even Analysis
When does the 5080’s faster raw throughput justify its higher cost? The answer depends on whether you are throughput-constrained or budget-constrained.
At 8B model sizes, the 5080 delivers 13 extra tok/s but costs roughly $55 more per month. That extra throughput only pays for itself if you are processing over 1.5 million tokens daily and latency matters more than cost. For most batch-processing workloads, the 3090 wins on economics. Compare this against API pricing with our GPU vs API cost comparison tool.
For latency-critical applications serving real-time users, the 5080’s newer architecture and faster per-request response times could justify the premium. Review our best GPU for LLM inference guide for latency-focused recommendations.
Which GPU Should You Choose?
Choose the RTX 3090 if you need 24 GB VRAM for larger models, want the best throughput per dollar, or plan to run 13B+ quantised models on a single GPU. It remains the value champion for dedicated inference.
Choose the RTX 5080 if you run 7-8B models exclusively, need the latest architecture features, or prioritise per-request latency over cost efficiency. It delivers faster raw inference but at a higher price per token.
For workloads exceeding single-GPU capacity, explore multi-GPU clusters. Use the LLM cost calculator to estimate your total spend before committing.
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