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NVIDIA RTX 6000 Ada Generation

NVIDIA · 48GBGDDR6 ECC · Can run 97 models

BuyAmazon
ManufacturerNVIDIA
VRAM48 GB
Memory TypeGDDR6 ECC
ArchitectureAda Lovelace
CUDA Cores18,176
Tensor Cores568
Bandwidth960 GB/s
TDP300W
MSRP$6,800
ReleasedDec 3, 2022

AI Notes

The RTX 6000 Ada is a flagship workstation GPU with 48GB of ECC VRAM. It can comfortably run 30B-parameter models at high quantizations and 70B models at Q4, making it a strong choice for professional AI inference. The high CUDA and tensor core count delivers excellent throughput for demanding workloads.

Compatible Models

ModelParametersBest QuantVRAM UsedFitEst. Speed
Qwen 3 0.6B600MQ4_K_M2.5 GBRuns~384 tok/s
Qwen 3.5 0.8B800MQ4_K_M1.5 GBRuns~640 tok/s
Gemma 3 1B1BQ8_02 GBRuns~480 tok/s
Llama 3.2 1B1BQ8_03 GBRuns~320 tok/s
DeepSeek R1 1.5B1.5BQ8_03 GBRuns~320 tok/s
SmolLM2 1.7B1.7BQ8_02.7 GBRuns~356 tok/s
Gemma 2 2B2BQ8_04 GBRuns~240 tok/s
Gemma 3n E2B2BQ4_K_M3.3 GBRuns~291 tok/s
Gemma 4 E2B2BQ4_K_M4 GBRuns~240 tok/s
Qwen 3.5 2B2BQ4_K_M3 GBRuns~320 tok/s
Llama 3.2 3B3BQ8_05 GBRuns~192 tok/s
StarCoder2 3B3BQ4_K_M3.5 GBRuns~274 tok/s
Phi-3 Mini 3.8B3.8BQ8_05.8 GBRuns~166 tok/s
Phi-4 Mini 3.8B3.8BQ4_K_M4.5 GBRuns~213 tok/s
Gemma 3 4B4BQ4_K_M5 GBRuns~192 tok/s
Gemma 3n E4B4BQ4_K_M4.5 GBRuns~213 tok/s
Gemma 4 E4B4BQ4_K_M6 GBRuns~160 tok/s
Qwen 3 4B4BQ4_K_M4.5 GBRuns~213 tok/s
Qwen 3.5 4B4BQ4_K_M4.5 GBRuns~213 tok/s
Yi 1.5 6B6BQ4_K_M5 GBRuns~192 tok/s
Codestral Mamba 7B7BQ4_K_M6.9 GBRuns~139 tok/s
DeepSeek R1 7B7BQ8_09 GBRuns~107 tok/s
Falcon 3 7B7BQ4_K_M6.8 GBRuns~141 tok/s
InternLM 2.5 7B7BQ4_K_M5.5 GBRuns~175 tok/s
Mistral 7B7BQ8_09 GBRuns~107 tok/s
OpenChat 3.5 7B7BQ4_K_M6.9 GBRuns~139 tok/s
Qwen 2.5 7B7BQ8_09 GBRuns~107 tok/s
Qwen 2.5 Coder 7B7BQ8_09 GBRuns~107 tok/s
Qwen 2.5 VL 7B7BQ4_K_M7 GBRuns~137 tok/s
StarCoder2 7B7BQ4_K_M5.5 GBRuns~175 tok/s
WizardLM 2 7B7BQ4_K_M6.9 GBRuns~139 tok/s
Aya Expanse 8B8BQ4_K_M6.5 GBRuns~148 tok/s
Cogito 8B8BQ4_K_M7.5 GBRuns~128 tok/s
DeepSeek R1 8B8BQ4_K_M7.5 GBRuns~128 tok/s
Dolphin 3 8B8BQ4_K_M6 GBRuns~160 tok/s
Granite 3.3 8B8BQ8_010 GBRuns~96 tok/s
Llama 3.1 8B8BQ8_010 GBRuns~96 tok/s
Nemotron 3 Nano 8B8BQ4_K_M7.5 GBRuns~128 tok/s
Nous Hermes 2 8B8BQ4_K_M6 GBRuns~160 tok/s
Qwen 3 8B8BQ4_K_M7.5 GBRuns~128 tok/s
Gemma 2 9B9BQ8_011 GBRuns~87 tok/s
Qwen 3.5 9B9BQ4_K_M7.5 GBRuns~128 tok/s
Yi 1.5 9B9BQ4_K_M6.5 GBRuns~148 tok/s
Yi Coder 9B9BQ4_K_M8 GBRuns~120 tok/s
Falcon 3 10B10BQ4_K_M8.5 GBRuns~113 tok/s
Llama 3.2 Vision 11B11BQ4_K_M8.5 GBRuns~113 tok/s
Gemma 3 12B12BQ4_K_M10.5 GBRuns~91 tok/s
Mistral Nemo 12B12BQ4_K_M9.5 GBRuns~101 tok/s
DeepSeek R1 14B14BQ4_K_M9.9 GBRuns~97 tok/s
Phi-4 14B14BQ4_K_M9.9 GBRuns~97 tok/s
Phi-4 Reasoning 14B14BQ4_K_M11 GBRuns~87 tok/s
Qwen 2.5 14B14BQ4_K_M9.9 GBRuns~97 tok/s
Qwen 2.5 Coder 14B14BQ4_K_M12 GBRuns~80 tok/s
Qwen 3 14B14BQ4_K_M12 GBRuns~80 tok/s
StarCoder2 15B15BQ8_017 GBRuns~56 tok/s
InternLM 2.5 20B20BQ4_K_M12 GBRuns~80 tok/s
gpt-oss 20B21BMXFP415 GBRuns~64 tok/s
Codestral 22B22BQ4_K_M14.7 GBRuns~65 tok/s
Devstral 24B24BQ4_K_M17 GBRuns~56 tok/s
Magistral Small 24B24BQ4_K_M17 GBRuns~56 tok/s
Mistral Small 3.1 24B24BQ4_K_M18 GBRuns~53 tok/s
Gemma 4 26B26BQ4_K_M20 GBRuns~48 tok/s
Gemma 2 27B27BQ4_K_M17.7 GBRuns~54 tok/s
Gemma 3 27B27BQ4_K_M20 GBRuns~48 tok/s
Qwen 3.5 27B27BQ4_K_M19 GBRuns~51 tok/s
Qwen 3.6 27B27BQ4_K_M20 GBRuns~48 tok/s
Qwen 3 30B-A3B (MoE)30BQ4_K_M22 GBRuns~44 tok/s
Gemma 4 31B31BQ4_K_M22 GBRuns~44 tok/s
Aya Expanse 32B32BQ4_K_M22 GBRuns~44 tok/s
Cogito 32B32BQ4_K_M21.5 GBRuns~45 tok/s
DeepSeek R1 32B32BQ4_K_M20.7 GBRuns~46 tok/s
Qwen 2.5 32B32BQ4_K_M20.7 GBRuns~46 tok/s
Qwen 2.5 Coder 32B32BQ4_K_M23 GBRuns~42 tok/s
Qwen 3 32B32BQ4_K_M23 GBRuns~42 tok/s
QwQ 32B32BQ4_K_M21.5 GBRuns~45 tok/s
Laguna XS 2.133BQ4_K_M22 GBRuns~44 tok/s
WizardCoder 33B33BQ4_K_M22 GBRuns~44 tok/s
Nous Hermes 2 34B34BQ4_K_M19 GBRuns~51 tok/s
Yi 1.5 34B34BQ4_K_M21 GBRuns~46 tok/s
Command R 35B35BQ4_K_M22.5 GBRuns~43 tok/s
Qwen 3.5 35B A3B35BQ4_K_M12 GBRuns~80 tok/s
Qwen 3.6 35B-A3B35BQ4_K_M27 GBRuns~36 tok/s
Dolphin Mixtral 8x7B47BQ4_K_M26 GBRuns~37 tok/s
Mixtral 8x7B47BQ4_K_M29.7 GBRuns~32 tok/s
Cogito 70B70BQ4_K_M43 GBRuns (tight)~22 tok/s
DeepSeek R1 70B70BQ4_K_M43.5 GBRuns (tight)~22 tok/s
Llama 3.1 70B70BQ4_K_M43.5 GBRuns (tight)~22 tok/s
Llama 3.3 70B70BQ4_K_M43.5 GBRuns (tight)~22 tok/s
Qwen 2.5 72B72BQ4_K_M44.7 GBRuns (tight)~21 tok/s
Qwen 2.5 VL 72B72BQ4_K_M41 GBRuns (tight)~23 tok/s
Llama 3.2 Vision 90B90BQ4_K_M50 GBCPU Offload~6 tok/s
Command R+ 104B104BQ4_K_M57 GBCPU Offload~5 tok/s
Llama 4 Scout (109B/17B active)109BQ4_K_M72 GBCPU Offload~4 tok/s
Command A 111B111BQ4_K_M61 GBCPU Offload~5 tok/s
gpt-oss 120B117BMXFP470 GBCPU Offload~4 tok/s
Devstral 2 123B123BQ4_K_M67 GBCPU Offload~4 tok/s
Mistral Large 2 123B123BQ4_K_M67 GBCPU Offload~4 tok/s
17 model(s) are too large for this hardware.