GeForce RTX 3090 vs RTX A6000 for Local AI

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Which GPU is better for local AI workloads?

The A6000's 48GB fits 70B models at Q4 comfortably — the cheapest single-card path to them. Its 768 GB/s bandwidth trails the 3090's 936, so shared models decode slightly slower. The 3090 remains the consumer value pick; the A6000 is the capacity workhorse.

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	RTX A6000	Difference
VRAM	24	48	-50%
Memory bandwidth	936	768	+22%
Memory type	GDDR6X	GDDR6	—
Memory bus	384	384	—
TDP	350	300	+17%
CUDA cores	10496	10752	-2%
Architecture	Ampere	Ampere	—
Launch MSRP	$1,499	$4,500	-67%
Street price	$1,499	$4,500	-67%

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

What about price?

Launch MSRP: $1499 (3090) versus $4500 (A6000). Used A6000s have fallen as datacenter cards retire.

Which one should you buy for LLMs and image generation?

NVIDIA · 2020

GeForce RTX 3090

Buy the used 3090 for 24GB value.

Full specs & benchmarks →

NVIDIA · 2021

RTX A6000

Buy the used A6000 when you need 48GB and multi-GPU stability (blower cooler).

Full specs & benchmarks →

Is it faster for LLM inference?

70B Q4 (~40GB) only on the A6000. 32B-class decode: 3090 slightly faster on bandwidth.

How does it handle image generation?

Both handle heavy pipelines; A6000's 48GB enables huge batches.

Which AI models fit on each card?

Computed from our model VRAM database at Q4 quantization: GeForce RTX 3090 has 24 GB, RTX A6000 has 48 GB.

Fit only on the RTX A6000 (48 GB)

AliceAI Foundation 80B A3B Base ✓ Qwen3-Coder-Next ✓ Kolibri 1 ✓ Qwen 2.5 72B Instruct ✓ Kimi-Dev-72B ✓ K2 Think V2 ✓ Llama 3.1 70B ✓ Llama 3.3 70B ✓ Kimi Linear 48B A3B Instruct ✓ Mixtral 8X7B ✓

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

3090 or A6000 for 70B models?

A6000: 70B at Q4 needs about 40GB — the 3090's 24GB requires offloading. The A6000 is the cheapest single-card 70B route.

Is the A6000 slower than a 3090?

Slightly, on paper: 768 versus 936 GB/s bandwidth. In practice its stability and 48GB capacity matter more for large-model work.