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NVIDIA A40 (48GB)

The NVIDIA A40 (48GB) features 48 GB of dedicated VRAM and 696 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

696 GB/s

Max Inference Class

32B 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 7B (7B)Sahabat-AI-Gemma2-9B (9.2B)OLMo 3.1 32B Think (32B)Ling 3.0 Flash VL (124B (5.5B active))

Precision:

Best Models to Run on NVIDIA A40 (48GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA A40 (48GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#33

Qwen 3.8 27B

27B

INT4
Q8

34 tok/s

20 tok/s

~2.2s

~2.3s

#48

Agnes 3.0 Flash

33B

INT4
Q8

28 tok/s

16 tok/s

~2.7s

~2.8s

#72

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4
Q8

145 tok/s

99 tok/s

~442 ms

~445 ms

#83

Qwen3.5-27B

27B

INT4
Q8

34 tok/s

20 tok/s

~2.2s

~2.3s

#92

Sarvam-30B

32B (2.4B active)

INT4
Q8

190 tok/s

141 tok/s

~309 ms

~310 ms

#94

Qwen3.6 35B A3B

35B (3B active)

INT4
Q8

170 tok/s

121 tok/s

~360 ms

~362 ms

#96

Muse Glimmer 30B

30B

INT4
Q8

31 tok/s

18 tok/s

~2.5s

~2.5s

#100

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

45 tok/s

30 tok/s

17 tok/s

~1.2s

~1.2s

~1.2s

#101

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

3 tok/s

2 tok/s

1 tok/s

~24.6s

~24.8s

~25.4s

#108

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8

155 tok/s

106 tok/s

~404 ms

~407 ms

#109

Gemma 4 12B

11.95B

INT4
Q8
FP16

52 tok/s

35 tok/s

20 tok/s

~1.0s

~1.0s

~1.0s

#113

Qwen3.5-9B

9B

INT4
Q8
FP16

92 tok/s

55 tok/s

29 tok/s

~767 ms

~774 ms

~791 ms

#114

Qwen3.5-35B-A3B

35B (3B active)

INT4
Q8

170 tok/s

121 tok/s

~360 ms

~362 ms

#118

K2 Horizon 7B

7B

INT4
Q8
FP16

113 tok/s

69 tok/s

37 tok/s

~601 ms

~606 ms

~619 ms

#128

NVIDIA Nemotron 3 Nano 30B-A3B

3.5B (30B active)

INT4
Q8
FP16

30 tok/s

17 tok/s

9 tok/s

~2.4s

~2.4s

~2.5s

#129

Gemma 4 31B

30.7B

INT4
Q8

30 tok/s

17 tok/s

~2.5s

~2.6s

#131

Nemotron 3.5 Lightning

30B (3B active)

INT4
Q8

65 tok/s

59 tok/s

~362 ms

~364 ms

#132

Qwen3.5-4B

4B

INT4
Q8
FP16

172 tok/s

110 tok/s

62 tok/s

~353 ms

~356 ms

~363 ms

#133

Qwen3-30B-A3B

30B (3B active)

INT4
Q8

172 tok/s

123 tok/s

~353 ms

~355 ms

#136

GPT-OSS 20B

21B (3.6B active)

INT4
Q8
FP16

72 tok/s

62 tok/s

40 tok/s

~386 ms

~388 ms

~462 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 A40 (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 696 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 155 tok/s on an 8B Q4 model and 18 tok/s on a 70B Q4 model.

Frequently Asked Questions