GeForce RTX 4090 vs RTX 6000 Ada for Local AI

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Which GPU is better for local AI workloads?

Same Ada architecture and 1008/960-class bandwidth; the 6000 Ada's 48GB holds 70B models at Q4 on one card, where the 4090's 24GB tops out at 32B. The 4090 is the consumer value monster; the 6000 Ada is for 48GB-per-slot density and workstation drivers.

Spec comparison: what actually differs

The table below is computed live from our hardware database. Positive deltas favor the GeForce RTX 4090.

Specification	GeForce RTX 4090	RTX 6000 Ada	Difference
VRAM	24	48	-50%
Memory bandwidth	1008	960	+5%
Memory type	GDDR6X	GDDR6	—
Memory bus	384	384	—
TDP	450	300	+50%
CUDA cores	16384	18176	-10%
Architecture	Ada Lovelace	Ada Lovelace	—
Launch MSRP	$1,599	$6,800	-76%
Street price	$1,599	$6,800	-76%

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

What about price?

Launch MSRP: $1599 (4090) versus $6800 (6000 Ada).

Which one should you buy for LLMs and image generation?

NVIDIA · 2022

GeForce RTX 4090

Buy the 4090 (used) unless you need 48GB per slot.

Full specs & benchmarks →

NVIDIA · 2023

RTX 6000 Ada

Buy the 6000 Ada for single-card 70B or 2-slot 96GB builds.

Full specs & benchmarks →

Is it faster for LLM inference?

70B Q4 needs the 48GB; 32B-class fits both (decode: 1008 vs 960 GB/s, slight 4090 edge).

How does it handle image generation?

Both elite; 6000 Ada's capacity enables production batching.

Which AI models fit on each card?

Computed from our model VRAM database at Q4 quantization: GeForce RTX 4090 has 24 GB, RTX 6000 Ada has 48 GB.

Fit only on the RTX 6000 Ada (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

4090 or RTX 6000 Ada for local 70B models?

6000 Ada: 70B at Q4 needs ~40GB, double the 4090's 24GB. Two used 3090s also reach it cheaper but need multi-GPU software work.

Why is the 6000 Ada so much more expensive?

Professional validation, 48GB of ECC memory, blower cooling for multi-card workstations, and workstation driver support — capacity plus ecosystem, not speed.