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NVIDIA RTX 6000 Ada Generation vs NVIDIA RTX PRO 6000 Blackwell

For running models locally, priced in Germany. At 8k context the first runs 68 models fully in memory, the second 78.

In short

The NVIDIA RTX PRO 6000 Blackwell has more memory for models: 96 GB against 48 GB. At 8k context the NVIDIA RTX PRO 6000 Blackwell runs 78 of the models we track fully in memory and the NVIDIA RTX 6000 Ada Generation 68; only the NVIDIA RTX PRO 6000 Blackwell holds DeepSeek V4 Flash. On the models both run fully in memory, the NVIDIA RTX PRO 6000 Blackwell is about 1.9× as fast as the NVIDIA RTX 6000 Ada Generation (estimated, median across models).

Updated . Speeds are estimates; how they are worked out.

NVIDIA RTX 6000 Ada Generation

Not priced here

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NVIDIA RTX PRO 6000 Blackwell

19.895 €

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Specifications

SpecNVIDIA RTX 6000 Ada GenerationNVIDIA RTX PRO 6000 Blackwell
GPURTX 6000 Ada GenerationRTX PRO 6000 Blackwell
Memory48 GB GDDR696 GB GDDR7
Memory bus384-bit512-bit
Memory bandwidth960 GB/s1,792 GB/s
FP16 compute91.1 TFLOPS504 TFLOPS
SoftwareCUDACUDA
Board power300 W600 W
PCIe generationGen 4Gen 5
PCIe lanesx16x16

What each runs

Estimated decode speed with the most precise quant that fits each one est.

ModelNVIDIA RTX 6000 Ada GenerationNVIDIA RTX PRO 6000 Blackwell
DeepSeek V4 Flashwon't run89tok/sUD-Q2_K_XL
Qwen 3.8 Flash Nextwon't run21tok/sUD-Q3_K_XL
Qwen 3.8 27B20tok/sQ8_037tok/sQ8_0
MiniMax M2.7offload117tok/sUD-Q2_K_XL
Qwen 3.5 122B-A10B97tok/sUD-Q2_K_XL79tok/sQ6_K
Qwen 3.6 27B20tok/sQ8_038tok/sQ8_0
Qwen3 Coder Next135tok/sQ4_K_M151tok/sQ8_0
Gemma 4 31B17tok/sQ8_033tok/sQ8_0
Nemotron 3 Super 120B-A12Boffload69tok/sQ5_K_M
gpt-oss 120Boffload195tok/sMXFP4
Mistral Small 4 119B139tok/sUD-Q2_K_XL110tok/sUD-Q6_K
Qwen 3.6 35B-A3B108tok/sQ8_0201tok/sQ8_0
Qwen 3.5 27B20tok/sQ8_038tok/sQ8_0
Gemma 4 26B-A4B79tok/sQ8_0147tok/sQ8_0
GLM 4.5 Air49tok/sQ2_K48tok/sQ6_K
Qwen3 Next 80B-A3B173tok/sQ4_K_M196tok/sQ8_0
Qwen 3.5 35B-A3B108tok/sQ8_0201tok/sQ8_0
GLM 4.7 Flash62tok/sQ8_0117tok/sQ8_0
Devstral Small 2 24B23tok/sQ8_042tok/sQ8_0
Nemotron 3.5 Lightning 30B-A3B104tok/sUD-Q6_K_XL195tok/sUD-Q6_K_XL
Kimi Linear 48B-A3B139tok/sQ6_K202tok/sQ8_0
gpt-oss 20B136tok/sMXFP4253tok/sMXFP4
Qwen3 Coder 30B-A3B89tok/sQ8_0167tok/sQ8_0
Qwen3 32B16tok/sQ8_030tok/sQ8_0
Gemma 4 12B45tok/sQ8_085tok/sQ8_0
Qwen 3.5 9B60tok/sQ8_0113tok/sQ8_0
Mistral Small 3.2 24B23tok/sQ8_042tok/sQ8_0
Gemma 3 27B20tok/sQ8_037tok/sQ8_0
DeepSeek R1 Distill Qwen 32B16tok/sQ8_030tok/sQ8_0
Llama 3.3 70B13tok/sQ4_K_M14tok/sQ8_0
Ministral 3 14B38tok/sQ8_071tok/sQ8_0
Qwen3 14B35tok/sQ8_065tok/sQ8_0
DeepSeek R1 0528 Qwen3 8B60tok/sQ8_0112tok/sQ8_0
Qwen3 8B60tok/sQ8_0112tok/sQ8_0
Phi-4 14B35tok/sQ8_064tok/sQ8_0
Qwen 3.5 4B124tok/sQ8_0231tok/sQ8_0
Ministral 3 8B59tok/sQ8_0109tok/sQ8_0
Granite 4.2 8B56tok/sQ8_0104tok/sQ8_0
Gemma 4 E4B70tok/sQ8_0131tok/sQ8_0
LFM2.5 8B-A1B244tok/sQ8_0455tok/sQ8_0
Llama 3.1 8B78tok/sQ6_K145tok/sQ6_K
Qwen2.5 0.5B942tok/sQ8_01759tok/sQ8_0
Qwen3.5 0.8B639tok/sQ8_01193tok/sQ8_0
Qwen3.5 2B383tok/sQ5_K_M715tok/sQ5_K_M
DeepSeek R1 Distill Qwen 1.5B280tok/sQ8_0522tok/sQ8_0
MiniCPM5 2B196tok/sQ8_0366tok/sQ8_0
LFM2.5 2.6B176tok/sQ8_0329tok/sQ8_0
LFM2.5 VL 3B176tok/sQ8_0329tok/sQ8_0
Spark X2.5 4B175tok/smlx-4bit327tok/smlx-4bit
granite vision 4.1 4b139tok/sQ8_0259tok/sQ8_0
Ministral 3 3B Instruct 2512132tok/sQ8_0246tok/sQ8_0
granite 4.2 3b130tok/sQ8_0243tok/sQ8_0
LFM2.5 24B A2B128tok/sQ8_0238tok/sQ8_0
Mellum2.1 12B A2.5B Thinking127tok/sQ8_0236tok/sQ8_0
FrogNano 4B 2609121tok/sQ8_0225tok/sQ8_0
Kimi VL A3B Thinking 2506106tok/sQ8_0199tok/sQ8_0
Kimi VL A3B102tok/smlx-8bit190tok/smlx-8bit
DeepSeek R1 Distill Qwen 7B70tok/sQ8_0130tok/sQ8_0
GLM 4 9B 041469tok/smlx-5bit129tok/smlx-5bit
DeepSeek R1 Distill Llama 8B62tok/sQ8_0116tok/sQ8_0
Ministral 3 8B Reasoning 251259tok/sQ8_0109tok/sQ8_0
GLM 4.6V Flash58tok/sQ8_0108tok/sQ8_0
GLM 4.1V 9B Thinking49tok/smlx-8bit92tok/smlx-8bit
Ministral 3 14B Reasoning 251238tok/sQ8_071tok/sQ8_0
DeepSeek R1 Distill Qwen 14B34tok/sQ8_064tok/sQ8_0
Qwen3.8 27B OBLITERATED28tok/smlx-4bit52tok/smlx-4bit
Devstral Small 250723tok/sQ8_042tok/sQ8_0
Magistral Small 250623tok/sQ8_042tok/sQ8_0
Magistral Small 250723tok/sQ8_042tok/sQ8_0
Magistral Small 250923tok/sQ8_042tok/sQ8_0
Muse Glimmer 30B18tok/sQ8_034tok/sQ8_0
granite 4.2 30b18tok/sQ8_033tok/sQ8_0
DeepSeek R1 Distill Llama 70B13tok/sQ4_K_M14tok/sQ8_0
GLM 4.5Voffload67tok/sQ4_K_M
GLM 4.6Voffload61tok/sQ4_K_M
Kimi Dev 72Boffload14tok/sQ8_0
Devstral 2 123B Instruct 2512offload12tok/sQ5_K_M
Mistral Medium 3.5 128Boffload12tok/sQ5_K_M
GLM 5.3 Flashwon't runoffload
MiniMax M2.5won't runoffload
DeepSeek R1won't runwon't run
DeepSeek R1 0528won't runwon't run
DeepSeek R1 Zerowon't runwon't run
DeepSeek V3won't runwon't run
DeepSeek V3 0324won't runwon't run
DeepSeek V3.1won't runwon't run
DeepSeek V3.1 Terminuswon't runwon't run
DeepSeek V3.2won't runwon't run
DeepSeek V4 Flash 0731won't runwon't run
DeepSeek V4 Flash Vision Expwon't runwon't run
GLM 4.5won't runwon't run
GLM 4.6won't runwon't run
GLM 4.7won't runwon't run
GLM 5won't runwon't run
GLM 5.1won't runwon't run
GLM 5.2won't runwon't run
GLM 5.3won't runwon't run
Kimi K2won't runwon't run
Kimi K2 Instruct 0905won't runwon't run
Kimi K2 Thinkingwon't runwon't run
Kimi K2.5won't runwon't run
MiniMax M2won't runwon't run
MiniMax M2.1won't runwon't run
MiniMax M3won't runwon't run

Questions

Which is better for local AI, the NVIDIA RTX 6000 Ada Generation or the NVIDIA RTX PRO 6000 Blackwell?
Choose the NVIDIA RTX PRO 6000 Blackwell to run bigger models. Choose the NVIDIA RTX PRO 6000 Blackwell for speed.
Which can run bigger models, the NVIDIA RTX 6000 Ada Generation or the NVIDIA RTX PRO 6000 Blackwell?
At 8k context the NVIDIA RTX PRO 6000 Blackwell runs 78 of the models we track fully in memory and the NVIDIA RTX 6000 Ada Generation 68; only the NVIDIA RTX PRO 6000 Blackwell holds DeepSeek V4 Flash.
Which is faster for local AI, the NVIDIA RTX 6000 Ada Generation or the NVIDIA RTX PRO 6000 Blackwell?
On the models both run fully in memory, the NVIDIA RTX PRO 6000 Blackwell is about 1.9× as fast as the NVIDIA RTX 6000 Ada Generation (estimated, median across models).

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