GeForce RTX 3090 vs GeForce RTX 4090 for Local AI

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Which GPU is better for local AI workloads?

Both hold 32B models at Q4 and 14B at Q8 in 24GB. The 4090's 1008 GB/s versus 936 GB/s plus far stronger compute makes it decisively faster per workload, but used pricing usually differs by hundreds of dollars. The 3090 remains the canonical budget 24GB AI card; verify VRAM thermal pad condition on used units.

Spec comparison: what actually differs

The table below is computed live from our hardware database. Positive deltas favor the GeForce RTX 3090.

Specification	GeForce RTX 3090	GeForce RTX 4090	Difference
VRAM	24	24	—
Memory bandwidth	936	1008	-7%
Memory type	GDDR6X	GDDR6X	—
Memory bus	384	384	—
TDP	350	450	-22%
CUDA cores	10496	16384	-36%
Architecture	Ampere	Ada Lovelace	—
Launch MSRP	$1,499	$1,599	-6%
Street price	$1,499	$1,599	-6%

Specs from our sourced product database. See how we source data.

What about price?

Launch MSRP: $1499 (3090, 2020) versus $1599 (4090, 2022). Both discontinued; check current used listings.

Which one should you buy for LLMs and image generation?

NVIDIA · 2020

GeForce RTX 3090

Buy the used 3090 for 24GB per dollar — budget king.

Full specs & benchmarks →

NVIDIA · 2022

GeForce RTX 4090

Buy the used 4090 if speed and prompt-processing matter more than savings.

Full specs & benchmarks →

Is it faster for LLM inference?

Identical model selection in 24GB; 4090 decodes ~8% faster on bandwidth and much faster on prompt processing (compute).

How does it handle image generation?

4090 clearly faster per image; 3090 batches equally well in 24GB.

Which AI models fit on each card?

Computed from our model VRAM database at Q4 quantization: GeForce RTX 3090 has 24 GB, GeForce RTX 4090 has 24 GB.

Fit on both cards

K2 Horizon 36B A4B (MoVA) Ornith 1.5 35B A3B Qwen3.6-35B-A3B Nex N2.5 Mini K2 Horizon 32B Edge0 35B A3B (preview) Qwen-AgentWorld-35B-A3B Agnes 3.0 Flash DeepSeek R1-Distill 32B Qwen 3 32B Qwen2 5 Coder 32B Olmo 3 32B Think Gemma 4 31B IT Xing4.0-29B-A4B Qwen3 30B A3B Qwen3-Coder-30B-A3B-Instruct Granite 4.2 30B Qwen3.8-27B Qwen3.5-27B Qwen3.6-27B Gemma 3 27B Wan 2.2 (A14B & TI2V-5B) OUI 1 Gemma 4 26B A4B IT Mistral Small 3.2 24B Devstral Small 2 24B Instruct Magistral-Small-2509 Gpt Oss 20B Maple Preview Ling Mini 2.0 Deepseek R1 Distill Qwen 14B Qwen3 14B Confucius4-T3PO WebWorld-14B ContextPilot 14B Phi 4 Phi 3 Medium Ministral 3 14B Instruct Ministral 3 14B Reasoning Gemma 3 12B Mellum 2.1 Thinking FLUX.1 dev Gemma 4 12B IT Ornith 1.5 9B OxCoder 9B Gemma 2 9B K2 Horizon 7B NeoHorse 1 9B Ministral 3 8B Reasoning Ministral 3 8B Instruct Granite 4.2 8B LFM2.5 8B A1B Qwen 3 8B ALoDLM 8B Deepseek R1 Distill Llama 8B Llama 3.1 8B Stable Diffusion 3.5 Large Gemma 4 E4B IT Edge0 8B A1B (preview) Ling-3.0-tiny Qwen2 5 7B ZGCM-1 7B Mistral 7B V03 Gemma 4 E2B IT K2 Horizon 3.7B Gemma 3 4B Ministral 3 3B Reasoning NeoHorse 1 4B Nanbeige4.2 3B Spark X2.5 4B Qwen3 4B Ministral 3 3B Instruct Granite 4.2 3B Stable Diffusion XL 1.0 Llama 3 2 3B Granite Swash 3B A600M LFM2.5 2.6B MiniCPM5 2B Qwen3 1 7B Spark X2.5 1.7B Whisper large-v3 Llama 3 2 1B K2 Horizon 0.9B Coqui XTTS-v2 Qwen3 0 6B GPT-X3 150M

Q4_K_M-equivalent sizes; context and quantization choices shift real limits. Check each model page for full quantization tables.

Frequently asked questions

Is a used RTX 3090 still good for AI in 2026?

Yes — 24GB VRAM runs 32B models at Q4, and it is usually the cheapest 24GB available. Check thermal-pad condition and expect 350W power draw.

How much faster is the 4090 than the 3090 for LLMs?

Decode is bandwidth-bound: 1008 vs 936 GB/s is ~8%. Prompt processing (compute-bound) is much faster on the 4090.

Which models fit on both cards?

Identical: 24GB holds 32B at Q4, 14B at Q8 with context, and 70B only with extreme quantization plus offloading.