RTX 4090 for LLMs: Which Models Fit? (2026)
The RTX 4090 has 24GB GDDR6X VRAM — the most VRAM in any consumer GPU. It can run models up to ~13B parameters at FP16, or up to ~70B at aggressive Q4 quantization. This guide covers exactly what fits and at what quality.
Models that fit in this GPU
| Model | Quantization | VRAM needed | Quality | Notes |
|---|---|---|---|---|
| Llama 3.1 8B | FP16 | 16GB | Full quality | Runs well with room for 8K context |
| Llama 4 Scout 17B active | Q4_K_M | 10GB | Good | MoE — only active params need VRAM |
| Qwen2.5 32B | Q4_K_M | 20GB | Good | Tight fit, limit context to 8K |
| Mistral Large 123B | Q2 | ~22GB | Degraded | Q2 quality is poor — not recommended |
| Llama 3.1 70B | Q4_K_M | ~43GB | Good | Requires 2× RTX 4090 |
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Frequently Asked Questions
Can the RTX 4090 run 70B models?
A single RTX 4090 (24GB) cannot fit a 70B model — even at Q4_K_M you need ~43GB. With two RTX 4090s connected via NVLink (48GB total), you can run Llama 3.1 70B at Q4_K_M comfortably.
Is the RTX 4090 good for LLM inference?
It is the best consumer GPU for LLM inference. 24GB VRAM covers most 7B–34B models at good quantization. The bandwidth (1,008 GB/s) enables ~120 tokens/second on a 7B model. Only the RTX 5090 (32GB) offers more consumer VRAM.
Data verified 2026-08-20. GPU rental prices change frequently — verify before purchasing. See methodology.