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NVIDIA RTX PRO 5000 Blackwell (72GB)

The NVIDIA RTX PRO 5000 Blackwell (72GB) features 72 GB of dedicated VRAM and 1344 GB/s memory bandwidth, supporting CUDA and TensorRT-LLM runtimes for full-precision and quantized inference.

Memory

72 GB

Usable Memory Ceiling

72 GB

Bandwidth

1344 GB/s

Max Inference Class

70B+ Models (Q4)

TDP

300W

VRAM Scaling by Context Length

Estimated memory requirements as context length increases. Curves crossing above the reference line indicate Out of Memory (OOM).

DeepSeek-R1 14B (14B)Nemotron 3.5 Lightning 30B A3B (30B (3B active))Llama 3.1 70B (70B)Step 3.5 Flash (196.81B (11B active))

Precision:

Best Models to Run on NVIDIA RTX PRO 5000 Blackwell (72GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA RTX PRO 5000 Blackwell (72GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#33

Qwen 3.8 27B

27B

INT4
Q8
FP16

62 tok/s

37 tok/s

20 tok/s

~1.1s

~1.1s

~1.2s

#48

Agnes 3.0 Flash

33B

INT4
Q8
FP16

52 tok/s

31 tok/s

16 tok/s

~1.4s

~1.4s

~1.5s

#61

Sarvam-105B

106B (10.3B active)

INT4

125 tok/s

~573 ms

#72

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4
Q8
FP16

188 tok/s

146 tok/s

87 tok/s

~224 ms

~226 ms

~267 ms

#75

Ling 3.0 Flash VL

124B (5.5B active)

INT4

165 tok/s

~443 ms

#83

Qwen3.5-27B

27B

INT4
Q8
FP16

62 tok/s

37 tok/s

20 tok/s

~1.1s

~1.1s

~1.2s

#84

Qwen3.5-122B-A10B

122B (10B active)

INT4

97 tok/s

~637 ms

#92

Sarvam-30B

32B (2.4B active)

INT4
Q8
FP16

221 tok/s

184 tok/s

126 tok/s

~158 ms

~159 ms

~174 ms

#94

Qwen3.6 35B A3B

35B (3B active)

INT4
Q8
FP16

207 tok/s

167 tok/s

105 tok/s

~183 ms

~184 ms

~218 ms

#96

Muse Glimmer 30B

30B

INT4
Q8
FP16

57 tok/s

34 tok/s

17 tok/s

~1.2s

~1.3s

~1.4s

#100

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

79 tok/s

55 tok/s

33 tok/s

~594 ms

~599 ms

~611 ms

#101

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

7 tok/s

4 tok/s

2 tok/s

~12.2s

~12.3s

~12.6s

#106

GLM-4.5-Air

106B (12B active)

INT4

37 tok/s

~767 ms

#108

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8
FP16

196 tok/s

152 tok/s

101 tok/s

~205 ms

~207 ms

~210 ms

#109

Gemma 4 12B

11.95B

INT4
Q8
FP16

88 tok/s

63 tok/s

38 tok/s

~509 ms

~514 ms

~524 ms

#113

Qwen3.5-9B

9B

INT4
Q8
FP16

137 tok/s

92 tok/s

53 tok/s

~385 ms

~389 ms

~397 ms

#114

Qwen3.5-35B-A3B

35B (3B active)

INT4
Q8
FP16

207 tok/s

167 tok/s

105 tok/s

~183 ms

~184 ms

~218 ms

#114

Ling 3.0 Flash

124B (5.1B active)

INT4

171 tok/s

~426 ms

#118

K2 Horizon 7B

7B

INT4
Q8
FP16

159 tok/s

110 tok/s

66 tok/s

~303 ms

~306 ms

~312 ms

#126

Qwen3 Next 80B A3B

80B (79B active)

INT4

19 tok/s

~3.2s

Inference Capacity

Maximum model parameter class runnable by weight precision.

Context:

4-bit

Q4

70B+ Models

8-bit

Q8

32B Models

16-bit

FP16

32B Models

Fine-Tuning Capacity

Hardware capacity for local training and adapter fine-tuning.

Context Length:

QLoRA

4-bit

70B+ Models

LoRA

16-bit

70B Models

Full Parameter Training

7B-8B Models

Workload Recommendations

Guidance for optimal precision and operational ceilings on NVIDIA RTX PRO 5000 Blackwell (72GB).

Inference Sweet Spot

Suitable for 70B parameter models at Q4/Q8 with extended 32k context windows, or high-throughput batching of smaller dense architectures.

Bandwidth & Speed Profile

With 1344 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 299 tok/s on an 8B Q4 model and 35 tok/s on a 70B Q4 model.

Frequently Asked Questions