RTX 6000 Ada vs RTX A6000 for Local AI

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Which GPU is better for local AI workloads?

Both are 48GB workstation cards that hold 70B models at Q4. The Ada generation decodes faster (960 vs 768 GB/s) and roughly doubles compute throughput. Used A6000s are the value 48GB play; the 6000 Ada is the current production card.

Spec comparison: what actually differs

The table below is computed live from our hardware database. Positive deltas favor the RTX 6000 Ada.

Specification	RTX 6000 Ada	RTX A6000	Difference
VRAM	48	48	—
Memory bandwidth	960	768	+25%
Memory type	GDDR6	GDDR6	—
Memory bus	384	384	—
TDP	300	300	—
CUDA cores	18176	10752	+69%
Architecture	Ada Lovelace	Ampere	—
Launch MSRP	$6,800	$4,500	+51%
Street price	$6,800	$4,500	+51%

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

What about price?

Launch MSRP: $6800 (6000 Ada) versus $4500 (A6000). Used A6000 pricing has dropped with datacenter refreshes.

Which one should you buy for LLMs and image generation?

NVIDIA · 2023

RTX 6000 Ada

Buy a used A6000 for cheapest 48GB.

Full specs & benchmarks →

NVIDIA · 2021

RTX A6000

Buy the 6000 Ada for current-gen speed with the same 48GB.

Full specs & benchmarks →

Is it faster for LLM inference?

Same model set in 48GB; Ada decodes ~25% faster on bandwidth.

How does it handle image generation?

Ada clearly faster per image; capacity equal.

Which AI models fit on each card?

Computed from our model VRAM database at Q4 quantization: RTX 6000 Ada has 48 GB, RTX A6000 has 48 GB.

Fit on both cards

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 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

A6000 or RTX 6000 Ada for 70B models?

Both fit 70B at Q4 in 48GB. The Ada is ~25% faster on bandwidth-bound decode; the used A6000 is the budget route to the same capacity.