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NVIDIA RTX 5090 (32GB)

The NVIDIA RTX 5090 (32GB) features 32 GB of dedicated VRAM and 1792 GB/s memory bandwidth, supporting CUDA and TensorRT-LLM runtimes for full-precision and quantized inference.

Memory

32 GB

Usable Memory Ceiling

32 GB

Bandwidth

1792 GB/s

Max Inference Class

32B Models (Q4)

TDP

575W

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 7B (7B)LensVLM 9B (9B)Nemotron 3.5 Lightning (30B (3B active))Qwen3 Next 80B A3B (80B (79B active))

Precision:

Best Models to Run on NVIDIA RTX 5090 (32GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA RTX 5090 (32GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#33

Qwen 3.8 27B

27B

INT4
Q8

78 tok/s

46 tok/s

~723 ms

~792 ms

#49

Agnes 3.0 Flash

33B

INT4

67 tok/s

~878 ms

#72

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4

214 tok/s

~147 ms

#80

Qwen3.5-27B

27B

INT4
Q8

78 tok/s

46 tok/s

~723 ms

~792 ms

#84

MiMo V2 Flash

15B (309B active)

INT4
Q8

9 tok/s

5 tok/s

~7.8s

~7.9s

#87

Sarvam-30B

32B (2.4B active)

INT4

245 tok/s

~104 ms

#90

Muse Glimmer 30B

30B

INT4
Q8

72 tok/s

39 tok/s

~801 ms

~948 ms

#95

Qwen3.6 35B A3B

35B (3B active)

INT4

232 tok/s

~121 ms

#96

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

98 tok/s

70 tok/s

38 tok/s

~386 ms

~390 ms

~466 ms

#102

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8

222 tok/s

169 tok/s

~135 ms

~147 ms

#110

Qwen3.5-35B-A3B

35B (3B active)

INT4

232 tok/s

~121 ms

#117

Qwen3.5-9B

9B

INT4
Q8
FP16

163 tok/s

113 tok/s

68 tok/s

~251 ms

~254 ms

~260 ms

#119

K2 Horizon 7B

7B

INT4
Q8
FP16

186 tok/s

134 tok/s

83 tok/s

~198 ms

~200 ms

~205 ms

#124

Gemma 4 12B

11.95B

INT4
Q8
FP16

108 tok/s

79 tok/s

46 tok/s

~331 ms

~335 ms

~371 ms

#127

Gemma 4 31B

30.7B

INT4
Q8

71 tok/s

39 tok/s

~819 ms

~969 ms

#130

Nemotron 3.5 Lightning

30B (3B active)

INT4

128 tok/s

~122 ms

#131

Qwen3.5-4B

4B

INT4
Q8
FP16

234 tok/s

183 tok/s

124 tok/s

~119 ms

~120 ms

~123 ms

#133

GPT-OSS 20B

21B (3.6B active)

INT4
Q8

139 tok/s

118 tok/s

~129 ms

~141 ms

#139

NVIDIA Nemotron 3 Nano 30B-A3B

3.5B (30B active)

INT4
Q8
FP16

69 tok/s

43 tok/s

23 tok/s

~777 ms

~786 ms

~805 ms

#143

Qwen3-30B-A3B

30B (3B active)

INT4
Q8

234 tok/s

175 tok/s

~119 ms

~139 ms

Inference Capacity

Maximum model parameter class runnable by weight precision.

Context:

4-bit

Q4

32B Models

8-bit

Q8

14B Models

16-bit

FP16

14B Models

Fine-Tuning Capacity

Hardware capacity for local training and adapter fine-tuning.

Context Length:

QLoRA

4-bit

32B Models

LoRA

16-bit

14B Models

Full Parameter Training

3B Models

Workload Recommendations

Guidance for optimal precision and operational ceilings on NVIDIA RTX 5090 (32GB).

Inference Sweet Spot

Optimized for 14B models at uncompressed or Q8 precision, and 32B models at Q4 with up to 32k context.

Bandwidth & Speed Profile

With 1792 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 398 tok/s on an 8B Q4 model and 47 tok/s on a 70B Q4 model.

Frequently Asked Questions