RTX 3090 AI Inference Benchmarks (2024)

Short answer

The NVIDIA RTX 3090 is still a strong choice for AI inference in 2024. It offers 24GB VRAM, good FP16 throughput, and solid price/performance versus datacenter cards. For single-GPU LLM work it runs many 7B–13B models fast. Two 3090s with NVLink can improve multi-GPU LLM serving by roughly 40–60% in practice, but large 70B models still need quantization or model sharding to run.

What this article covers

• Real-world benchmarks and what they mean.
• Which LLM sizes the 3090 can run and expected tokens/sec.
• How NVLink and PCIe affect inference speed.
• Simple setup and power tips to get the best efficiency.

Why the RTX 3090 matters

The 3090 has a rare combo: high CUDA/tensor core counts and 24GB of VRAM. That VRAM size lets you load larger models without complex offloading. Many cloud and hobby rigs still use 3090s because they are cheaper than an A100/H100 but can deliver strong throughput for inference. See the real-world testing in The Register.

Benchmarks summary (short)

These are practical, approximate outcomes you can expect from 1x 3090, and from 2x 3090 with NVLink. Numbers vary by model, quantization, and software. Use this as a planning guide, not a guarantee.

Model	1x RTX 3090 (typ)	2x RTX 3090 NVLink (typ)	Notes
7B LLM (Q4_K_M)	~90–130 tokens/sec	~160–220 tokens/sec	Runs easily, little quantization needed
13B LLM (Q4_K_M)	~35–70 tokens/sec	~60–110 tokens/sec	Works with Q4 or FP16
30B LLM	May need tensor parallel or heavy quant	Often fits with NVLink and tensor-parallel	Memory can be tight; use vLLM and QWen settings
70B LLM	Usually OOM or very slow	Possible with NVLink + quant + tensor-parallel	Expect complexity; cloud A40/A6000 or H100 often better
ResNet50 inference	Strong FP16/INT8 perf	Nearly double with 2x in many setups	See deep CV benchmarks in ServeTheHome

Where these numbers come from

I synthesized published test runs and community benchmarks. Useful sources include community tests on GitHub GPU Benchmarks, the Bizon Tech comparisons, and hands-on vLLM reports like Himesh Prasad. Community threads on Reddit also show practical per-model throughput numbers.

How NVLink helps in practice

NVLink gives faster GPU-to-GPU bandwidth than PCIe alone. For some inference setups, that matters a lot. Tests show a pair of 3090s with NVLink can boost inference throughput by around 40–60% compared to two cards without NVLink in certain tensor-parallel scenarios.

For 4x 3090 rigs the benefit drops because more traffic goes over PCIe. See the NVLink tests in Himesh's vLLM benchmark and NVLink analysis at ServeTheHome.

FP16, FP32, INT8 and quantization

3090 tensor cores do well in FP16. Many inference stacks use quantization (Q4_K_M, Q8, etc.) to cut memory and speed up inference. That is why a 3090 can run bigger models than its raw FLOPS suggest.

For most LLM inference, test Q4 or FP16 first. Use libraries like the community benchmarks to compare quant types.

PCIe bandwidth and CPU pairing

LLM inference sometimes needs a good CPU and PCIe bandwidth, especially when you run many parallel streams or use multi-GPU tensor parallelism. Reddit and forum threads note that PCIe bandwidth can bottleneck multi-GPU setups, so pair the 3090 with a modern CPU and a PCIe 4.0 or better platform when possible. See discussion on PCIe and local LLaMA rigs in community posts.

Practical setup tips to get the best inferencing performance

1. Power limit: Set a modest power limit for efficiency. Tests show a sweet spot around 220W per 3090 for many inference loads. That saves power while keeping high throughput. See vLLM findings.
2. Drivers and CUDA: Use stable NVIDIA drivers and CUDA versions that your inference stack supports. Match cuDNN and PyTorch/TensorRT versions recommended by vendors like Lambda.
3. NVLink if you need multi-GPU memory pooling: Use NVLink for pairs of 3090s when you want combined memory or faster tensor-parallel runs.
4. Use quantization: Q4_K_M or Q8 can unlock larger model sizes. Community benchmarks and vLLM examples show big gains.
5. Monitor memory: Use tools like nvidia-smi to watch VRAM and avoid silent OOMs.

Software stacks that work well

• vLLM for model serving and high throughput.
• Transformers + bitsandbytes or GGUF loaders for quantized models.
• TorchServe or Triton for production endpoints.

See practical vLLM examples in Himesh's post and broad GPU comparisons at GitHub.

Price vs performance—when to pick a 3090

Choose a 3090 if you need a cost-efficient card with 24GB VRAM for development, small production services, or local experiments. If you need 70B-level inference at scale, consider renting A40, A6000, or H100 instances. Benchmarks comparing 3090 to higher-end cards are at Bizon Tech and ServeTheHome.

Common questions

Can one 3090 run Llama 70B?

Not comfortably. A single 3090 typically runs out of memory. With heavy quantization and optimized sharding you might squeeze it, but it is complex. Two 3090s with NVLink plus tensor parallelism help, but large production loads usually use datacenter GPUs.

Does NVLink always double performance?

No. NVLink helps for inter-GPU traffic heavy jobs, but gains depend on the model and parallelism method. Some tests show near-double gains for two cards on specific tasks, while others show 10% gains for 4-card setups. See practical results in vLLM and ServeTheHome.

Quick checklist before you buy

• Which models do you want to run? 7B/13B are safe on one 3090.
• Will you need multi-user serving? Consider NVLink and tensor parallelism.
• Do you want low cost per token? Compare 3090 to rented A6000 or cloud A100/H100.
• Plan for power limits and driver versions.

Final takeaway

The RTX 3090 is a capable, cost-effective inference GPU in 2024. It excels for single-GPU LLMs and can be pushed further with NVLink and smart quantization. Use community benchmarks and measure your own workload. If you need many 70B inferences or minimal ops complexity, consider higher-end data-center GPUs.

Sources: ServeTheHome, Himesh Prasad vLLM, GitHub GPU Benchmarks, The Register, community threads on Reddit.