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NVIDIA L20 (48GB)

The NVIDIA L20 (48GB) features 48 GB of dedicated VRAM and 864 GB/s memory bandwidth, supporting CUDA and TensorRT-LLM runtimes for full-precision and quantized inference.

Memory

48 GB

Usable Memory Ceiling

48 GB

Bandwidth

864 GB/s

Max Inference Class

32B Models (Q4)

TDP

275W

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)Falcon2-11B (11B)OLMo 3.1 32B Think (32B)Ling 3.0 Flash VL (124B (5.5B active))

Precision:

Best Models to Run on NVIDIA L20 (48GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA L20 (48GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#36

Qwen 3.8 27B

27B

INT4
Q8

42 tok/s

24 tok/s

~2.8s

~2.8s

#80

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4
Q8

171 tok/s

119 tok/s

~550 ms

~553 ms

#86

Qwen3.5-27B

27B

INT4
Q8

42 tok/s

24 tok/s

~2.8s

~2.8s

#93

Sarvam-30B

32B (2.4B active)

INT4
Q8

221 tok/s

166 tok/s

~384 ms

~385 ms

#99

Muse Glimmer 30B

30B

INT4
Q8

38 tok/s

22 tok/s

~3.1s

~3.1s

#102

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

55 tok/s

37 tok/s

21 tok/s

~1.5s

~1.5s

~1.5s

#103

Qwen3.6 35B A3B

35B (3B active)

INT4
Q8

199 tok/s

144 tok/s

~448 ms

~450 ms

#105

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

4 tok/s

2 tok/s

1 tok/s

~30.6s

~30.8s

~31.3s

#110

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8

183 tok/s

126 tok/s

~503 ms

~506 ms

#117

Nemotron 3.5 Bolt 74B A9B

74B

INT4

15 tok/s

~8.3s

#120

Qwen3.5-35B-A3B

35B (3B active)

INT4
Q8

199 tok/s

144 tok/s

~448 ms

~450 ms

#121

Qwen3.5-9B

9B

INT4
Q8
FP16

110 tok/s

67 tok/s

36 tok/s

~956 ms

~962 ms

~975 ms

#127

K2 Horizon 7B

7B

INT4
Q8
FP16

135 tok/s

83 tok/s

45 tok/s

~748 ms

~753 ms

~763 ms

#130

Gemma 4 12B

11.95B

INT4
Q8
FP16

63 tok/s

43 tok/s

25 tok/s

~1.3s

~1.3s

~1.3s

#136

Qwen3.5-4B

4B

INT4
Q8
FP16

201 tok/s

132 tok/s

75 tok/s

~439 ms

~442 ms

~448 ms

#139

GPT-OSS 20B

21B (3.6B active)

INT4
Q8
FP16

88 tok/s

76 tok/s

49 tok/s

~477 ms

~478 ms

~567 ms

#140

GLM-4V

9B

INT4
Q8
FP16

80 tok/s

55 tok/s

32 tok/s

~959 ms

~965 ms

~978 ms

#145

NVIDIA Nemotron 3 Nano 30B-A3B

3.5B (30B active)

INT4
Q8
FP16

36 tok/s

21 tok/s

11 tok/s

~3.0s

~3.0s

~3.1s

#150

Qwen3-30B-A3B

30B (3B active)

INT4
Q8

201 tok/s

146 tok/s

~439 ms

~441 ms

#152

Granite 4.2 8B

8B

INT4
Q8
FP16

121 tok/s

74 tok/s

40 tok/s

~851 ms

~856 ms

~868 ms

Inference Capacity

Maximum model parameter class runnable by weight precision.

Context:

4-bit

Q4

32B Models

8-bit

Q8

32B 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

7B-8B Models

Workload Recommendations

Guidance for optimal precision and operational ceilings on NVIDIA L20 (48GB).

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 864 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 192 tok/s on an 8B Q4 model and 23 tok/s on a 70B Q4 model.

Frequently Asked Questions