NVIDIA GeForce RTX 4090 — Local LLM Performance & Compatibility

Written by Jakub Rusinowski · Last updated July 12, 2026

Still the benchmark for consumer AI. 24 GB VRAM fits DeepSeek-R1-Distill-Qwen-32B and Qwen3 32B at Q4_K_M. Llama 4 Scout does not fit — its 109B total parameters need ~67 GB resident even though only 17B activate per token. 165 t/s on Llama 3.1 8B.

Technical Specifications

VRAM24 GB
Memory Bandwidth1008 GB/s
TDP450 W
ArchitectureAda Lovelace AD102
Release Year2022
MSRP at Launch$1,599
Inference Speed (Llama 3.1 8B Q4_K_M)86–165 tok/s (estimated)
Inference Speed (Llama 3.3 70B Q4_K_M)Does not fit — needs ~44 GB of 24 GB usable
Buy This HardwareNVIDIA GeForce RTX 4090 24GB — 24 GB VRAM · 450 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

Affiliate disclosure: Some links on this page are affiliate links — if you buy through them, LLM Configurator may earn a commission at no extra cost to you. As an Amazon Associate, LLM Configurator earns from qualifying purchases.
NVIDIA GeForce RTX 4090 24GB
24 GB VRAM · 450 W board power
2026 prices are volatile — check the current listing.
Check price on Amazon

LLMs Compatible with 24 GB VRAM

All models below run comfortably in 24 GB VRAM with Q4_K_M quantization.

Llama 3.1 FamilyLlama 3.1 8B Instruct · 6 GB VRAM · Q4_K_M · ollama run llama3.1
DeepSeek R1DeepSeek R1 Distill Qwen 32B · 20 GB VRAM · Q4_K_M · ollama run deepseek-r1:32b
Qwen 3Qwen 3 32B · 21 GB VRAM · Q4_K_M · ollama run qwen3:32b
Qwen 3.6Qwen 3.6 35B-A3B · 22 GB VRAM · Q4_K_M · ollama run qwen3.6:35b-a3b
Qwen 3.7Qwen 3.7 35B-A3B · 22 GB VRAM · Q4_K_M · qwen3-7
Gemma 3Gemma 3 27B Instruct · 17 GB VRAM · Q4_K_M · ollama run gemma3:27b
Gemma 4Gemma 4 31B · 20 GB VRAM · Q4_K_M · ollama run gemma4:31b
Mistral Small 3.1Mistral Small 3.1 24B · 15 GB VRAM · Q4_K_M · ollama run mistral-small3.1

Best Use Cases

Quick Start with Ollama

Install Ollama then run the recommended model for this GPU:

ollama run deepseek-r1:32b

FAQ

Can the NVIDIA GeForce RTX 4090 run local LLMs?

Yes — the NVIDIA GeForce RTX 4090 has 24 GB VRAM and runs Still the benchmark for consumer AI. 24 GB VRAM fits DeepSeek-R1-Distill-Qwen-32B and Qwen3 32B at Q4_K_M. Llama 4 Scout

How fast is the NVIDIA GeForce RTX 4090 for AI inference?

The NVIDIA GeForce RTX 4090 is estimated to run Llama 3.1 8B at 86–165 tok/s with Q4_K_M quantization. Llama 3.3 70B does not fit: it needs about 44 GB against 24 GB usable. These are modelled estimates, not measurements — see /en/methodology.

What LLMs can I run on 24 GB VRAM?

With 24 GB you can run: Llama 3.1 Family, DeepSeek R1, Qwen 3, Qwen 3.6, Qwen 3.7. Use Ollama for the easiest setup: ollama run deepseek-r1:32b.

Can I Run It? — NVIDIA GeForce RTX 4090

Compare Similar GPUs

VRAM Tier

Buying Guide

← All GPU Reviews | Check Your Hardware | Full Benchmarks | Can I Run It?