GPU × use-case guide · Reasoning / agentic

Is the RTX 4090 a good GPU for reasoning / agentic?

The RTX 4090 is a ada NVIDIA GPU with 24 GB GDDR6X (1008 GB/s), 165 TFLOPS BF16 / 330 TFLOPS FP8, and a 450 W TDP. Reasoning / agentic workloads care most about long output generations (chain-of-thought), so decode throughput and KV-cache headroom dominate. Here's how the RTX 4090 measures up.

What models fit on a single RTX 4090?

Weights only, reserving ~25% of the 24 GB for KV cache, activations and fragmentation. ✓ = fits on one card.

Largest single-card fit: Llama 3.1 8B at BF16, Qwen 2.5 14B at FP8, Gemma 2 27B at INT4. Bigger models need tensor-parallel across 4 cards.

RTX 4090 for reasoning / agentic, specifically

Reasoning / agentic is context-heavy, so the KV cache — not the weights — is what fills the 24 GB. On the RTX 4090 you'll trade context length against batch size: long prompts mean fewer concurrent requests. Because it's latency-sensitive, run it at low batch sizes on a fast framework (vLLM ≈ 85% MFU) rather than maximising batch. Size it precisely on the calculator.

RTX 4090 pricing across providers

Verdict

At 24 GB, the RTX 4090 is a value card best suited to smaller reasoning / agentic models (up to Gemma 2 27B with INT4 quantisation). For larger models you'll want more VRAM or multi-GPU.