RTX 3090 for LLM Inference: What You Can Run

RTX 3090 Specs for LLM Inference

The RTX 3090 remains one of the most popular GPUs for self-hosted LLM inference, and for good reason. With 24GB of GDDR6X VRAM and strong compute throughput, it hits a price-to-performance sweet spot that few cards can match. If you need a dedicated GPU server for running language models, the 3090 is often the first card to consider.

Ampere architecture delivers 35.6 TFLOPS of FP32 performance and 142 TFLOPS of tensor operations with sparsity. The 936 GB/s memory bandwidth keeps tokens flowing even with large batch sizes. For inference workloads specifically, memory capacity matters more than raw compute, and 24GB opens the door to models that smaller cards simply cannot handle.

LLMs You Can Run on 24GB VRAM

The table below maps popular language models to their VRAM requirements at different precision levels, showing what fits on a single RTX 3090.

The sweet spot for the RTX 3090 is 7B-13B models at FP16, or up to 34B models with aggressive quantisation. For a deeper look at Llama sizing, see our Llama 3 VRAM requirements guide or check whether the RTX 3090 can run Llama 3 70B.

Tokens-per-Second Benchmarks

Raw VRAM capacity tells you what fits. Tokens per second tells you whether the experience is usable. These benchmarks use vLLM and llama.cpp on a dedicated RTX 3090 server.

Use our tokens-per-second benchmark tool to compare these numbers against other GPU configurations.

Quantisation Strategies for 24GB

Quantisation is how you unlock larger models on 24GB of VRAM. The key formats to know are GPTQ (GPU-optimised), AWQ (activation-aware), and GGUF (CPU offloading supported). INT4 quantisation typically reduces model size by 75% compared to FP16 with only a small quality loss.

For the RTX 3090, the best approach is to run 7B-8B models at FP16 for maximum quality, or use INT4 quantisation to squeeze in 30B+ parameter models. The VRAM cost guide covers the full trade-off picture.

Context length also matters. A Llama 3 8B model at FP16 uses about 16GB at 2K context, but extending to 8K context pushes VRAM usage closer to 20GB as the KV cache grows. Plan your deployment around your expected context window.

RTX 3090 vs Other GPUs for Inference

How does the 3090 stack up against the rest of the consumer and prosumer GPU range for inference workloads?

For detailed GPU matchups, check the GPU comparisons tool or read our guide on the best GPU for LLM inference.

Recommendations and Hosting Setup

The RTX 3090 is the ideal choice for running 7B-8B parameter models at full precision, or 30B+ models with INT4 quantisation. It offers excellent cost efficiency for inference compared to newer cards with smaller VRAM pools.

Pair the 3090 with at least 32GB of system RAM and NVMe storage for fast model loading. For production deployments, use vLLM or TGI for optimised batched inference. For experimentation, llama.cpp with GGUF models gives maximum flexibility.

Check the cost per million tokens calculator to estimate running costs for your workload, and explore GPU comparison guides if you need help choosing between the 3090 and newer alternatives.