ApX logoApX logo

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 (3B active))Llama 3.3 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

#35

Qwen 3.8 Flash

125B (6B active)

INT4

160 tok/s

~288 ms

#48

Sarvam-105B

106B (10.3B active)

INT4

119 tok/s

~621 ms

#63

Ling 3.0 Flash VL

124B (5.5B active)

INT4

157 tok/s

~479 ms

#72

Sarvam-30B

32B (2.4B active)

INT4
Q8
FP16

221 tok/s

184 tok/s

119 tok/s

~158 ms

~159 ms

~188 ms

#73

Muse Glimmer 30B

30B

INT4
Q8
FP16

57 tok/s

34 tok/s

17 tok/s

~1.2s

~1.3s

~1.4s

#77

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

7 tok/s

4 tok/s

2 tok/s

~12.2s

~12.3s

~12.6s

#78

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

79 tok/s

55 tok/s

33 tok/s

~594 ms

~599 ms

~611 ms

#83

Ling 3.0 Flash

124B (5.1B active)

INT4

162 tok/s

~461 ms

#93

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

#98

Gemma 4 12B

11.95B

INT4
Q8
FP16

88 tok/s

63 tok/s

38 tok/s

~509 ms

~514 ms

~524 ms

#101

Nemotron 3.5 Lightning

30B (3B active)

INT4
Q8
FP16

105 tok/s

97 tok/s

70 tok/s

~185 ms

~185 ms

~219 ms

#103

GPT-OSS 20B

21B (3.6B active)

INT4
Q8
FP16

115 tok/s

102 tok/s

77 tok/s

~196 ms

~197 ms

~200 ms

#110

Qwen3 Next 80B A3B

80B (79B active)

INT4

18 tok/s

~3.5s

#112

Gemma 4 31B

30.7B

INT4
Q8
FP16

56 tok/s

33 tok/s

17 tok/s

~1.3s

~1.3s

~1.4s

#121

NVIDIA Nemotron 3 Nano 30B-A3B

3.5B (30B active)

INT4
Q8
FP16

54 tok/s

33 tok/s

18 tok/s

~1.2s

~1.2s

~1.2s

#123

Granite 4.2 8B

8B

INT4
Q8
FP16

147 tok/s

100 tok/s

59 tok/s

~344 ms

~347 ms

~354 ms

#124

Qwen3-30B-A3B

30B (3B active)

INT4
Q8
FP16

208 tok/s

168 tok/s

111 tok/s

~180 ms

~181 ms

~199 ms

#126

Qwen2.5-72B

72B

INT4

26 tok/s

~2.9s

#128

Hunyuan Standard

52B (389B active)

INT4
Q8

5 tok/s

3 tok/s

~15.3s

~16.8s

#129

DeepSeek-R1 70B

70B

INT4

28 tok/s

~2.9s

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