GeForce RTX 3080 10GB vs GeForce RTX 3090 for Local AI

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Which GPU is better for local AI workloads?

The 3080's 10GB handles 8B models and SD 1.5 but strains on SDXL; the 3090 runs 32B LLMs at Q4 comfortably. The 3080's 760 GB/s bandwidth is respectable, but capacity rules inference. Used 3090 pricing reflects its AI demand.

Spec comparison: what actually differs

The table below is computed live from our hardware database. Positive deltas favor the GeForce RTX 3080 10GB.

Specification	GeForce RTX 3080 10GB	GeForce RTX 3090	Difference
VRAM	10	24	-58%
Memory bandwidth	760	936	-19%
Memory type	GDDR6X	GDDR6X	—
Memory bus	320	384	—
TDP	320	350	-9%
CUDA cores	8704	10496	-17%
Architecture	Ampere	Ampere	—
Launch MSRP	$699	$1,499	-53%
Street price	$699	$1,499	-53%

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

What about price?

Launch MSRP: $699 (3080) versus $1499 (3090). Used prices vary; check current listings.

Which one should you buy for LLMs and image generation?

NVIDIA · 2020

GeForce RTX 3080 10GB

Buy a cheap used 3080 for 8B/SD1.5 budgets.

Full specs & benchmarks →

NVIDIA · 2020

GeForce RTX 3090

Buy the used 3090 — the budget 24GB standard.

Full specs & benchmarks →

Is it faster for LLM inference?

8B class both; 14B/32B only on 3090.

How does it handle image generation?

SDXL marginal on 10GB; comfortable on 24GB.

Which AI models fit on each card?

Computed from our model VRAM database at Q4 quantization: GeForce RTX 3080 10GB has 10 GB, GeForce RTX 3090 has 24 GB.

Fit only on the GeForce RTX 3090 (24 GB)

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 ✓

Fit on both cards

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

3080 vs 3090 for LLMs?

3090: 24GB holds 32B at Q4 while the 3080's 10GB stops near 8B. The 3080 suits budget image generation with SD 1.5.

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