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What LLMs can an RTX PRO 6000 Blackwell run?

An RTX PRO 6000 Blackwell gives a model 96 GB of memory and 1,792 GB/s of bandwidth. Of the 40 open models tracked here, 24 fit at Q4_K_M with an 8,192-token context, and 1 more at a lower precision.

Models that fit on one RTX PRO 6000 Blackwell

The most precise weights that still fit with 8K tokens of context and one request, the memory that takes, the longest context at that precision and the writing speed. Bigger models come first.

Model Best precision Memory Longest context Tokens/s
Qwen3.8 Flash Next 180.0B, 6B active GGUF Q3_K_M 90.8 GB 208K 136–250
Mistral Medium 3.5 128B 127.7B GGUF Q4_K_M 82.7 GB 43K 12–17
Qwen3.5 122B-A10B 125.1B, 10B active GGUF Q5_K_M 91.5 GB 181K 66–116
Nemotron 3 Super 120B-A12B 123.6B, 12B active GGUF Q5_K_M 90.3 GB 256K (full) 57–99
gpt-oss-120b 116.8B, 5.1B active GGUF Q5_K_M 85.6 GB 128K (full) 114–205
Qwen3-Coder-Next 79.7B, 3B active GGUF Q8_0 87.4 GB 256K (full) 128–234
Llama 3.1 70B 70.6B GGUF Q8_0 80.0 GB 54K 12–17
Qwen3.6 35B-A3B 36.0B, 3B active FP16 / BF16 74.3 GB 256K (full) 77–135
Nemotron 3 Nano 30B-A3B 31.6B, 3.5B active FP16 / BF16 65.3 GB 256K (full) 68–119
Gemma 4 31B 31.3B FP16 / BF16 65.8 GB 256K (full) 15–21
GLM-4.7 Flash 31.2B, 3B active FP16 / BF16 64.9 GB 198K (full) 74–130
Xing 4.0 29B-A4B 31.2B, 4B active FP16 / BF16 64.8 GB 256K (full) 59–102
Qwen3 30B-A3B 30.5B, 3.3B active FP16 / BF16 63.9 GB 40K (full) 65–114
Muse Glimmer 30B 29.8B FP16 / BF16 61.7 GB 128K (full) 16–22
Qwen3.6 27B 27.8B FP16 / BF16 58.0 GB 256K (full) 17–24
Qwen3.8 27B 27.8B FP16 / BF16 58.0 GB 256K (full) 17–24
Gemma 4 26B-A4B 25.8B, 4B active FP16 / BF16 53.7 GB 256K (full) 59–102
gpt-oss-20b 20.9B, 3.6B active FP16 / BF16 43.6 GB 128K (full) 65–114
Gemma 4 12B 12.0B FP16 / BF16 25.4 GB 256K (full) 38–54
Qwen3.5 9B 9.7B FP16 / BF16 20.6 GB 256K (full) 47–66
Qwen3 8B 8.2B FP16 / BF16 18.5 GB 40K (full) 52–74
Llama 3.1 8B 8.0B FP16 / BF16 18.1 GB 128K (full) 53–75
Gemma 4 E4B 8.0B FP16 / BF16 17.0 GB 128K (full) 56–80
Nemotron 3 Nano 4B 4.0B FP16 / BF16 8.78 GB 256K (full) 103–154
MiniCPM5 2B 2.5B FP16 / BF16 6.02 GB 128K (full) 144–222

Too big for one card

How many RTX PRO 6000 Blackwell cards these need at Q4_K_M in one tensor-parallel group; some need more than 8.

How these numbers are worked out

Memory is the weights at each precision plus the KV cache for 8,192 tokens in FP16 and runtime overhead (0.5 GB plus 10%), computed from each model's files on Hugging Face with the LLM VRAM Calculator. Speed is estimated from memory bandwidth and the parameters read per token with the LLM Speed Calculator. Both tools take any other model from Hugging Face.

Other GPUs and Macs

Model numbers read from Hugging Face on .