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NVIDIA A16 (64GB)

The NVIDIA A16 (64GB) features 64 GB of dedicated VRAM and 800 GB/s memory bandwidth, supporting CUDA and TensorRT-LLM runtimes for full-precision and quantized inference.

Memory

64 GB

Usable Memory Ceiling

64 GB

Bandwidth

800 GB/s

Max Inference Class

70B+ Models (Q4)

TDP

250W

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 A16 (64GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA A16 (64GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#33

Qwen 3.8 27B

27B

INT4
Q8
FP16

39 tok/s

22 tok/s

11 tok/s

~4.6s

~4.7s

~5.1s

#48

Agnes 3.0 Flash

33B

INT4
Q8
FP16

33 tok/s

18 tok/s

9 tok/s

~5.6s

~5.7s

~6.7s

#61

Sarvam-105B

106B (10.3B active)

INT4

86 tok/s

~2.6s

#72

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4
Q8

161 tok/s

112 tok/s

~908 ms

~911 ms

#75

Ling 3.0 Flash VL

124B (5.5B active)

INT4

130 tok/s

~2.0s

#83

Qwen3.5-27B

27B

INT4
Q8
FP16

39 tok/s

22 tok/s

11 tok/s

~4.6s

~4.7s

~5.1s

#84

Qwen3.5-122B-A10B

122B (10B active)

INT4

64 tok/s

~2.9s

#92

Sarvam-30B

32B (2.4B active)

INT4
Q8
FP16

210 tok/s

157 tok/s

89 tok/s

~633 ms

~634 ms

~750 ms

#94

Qwen3.6 35B A3B

35B (3B active)

INT4
Q8

188 tok/s

136 tok/s

~739 ms

~741 ms

#96

Muse Glimmer 30B

30B

INT4
Q8
FP16

35 tok/s

20 tok/s

9 tok/s

~5.1s

~5.2s

~6.1s

#100

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

52 tok/s

35 tok/s

20 tok/s

~2.4s

~2.5s

~2.5s

#101

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

4 tok/s

2 tok/s

1 tok/s

~50.7s

~50.9s

~51.4s

#108

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8
FP16

173 tok/s

119 tok/s

66 tok/s

~830 ms

~833 ms

~911 ms

#109

Gemma 4 12B

11.95B

INT4
Q8
FP16

59 tok/s

40 tok/s

23 tok/s

~2.1s

~2.1s

~2.1s

#113

Qwen3.5-9B

9B

INT4
Q8
FP16

103 tok/s

62 tok/s

33 tok/s

~1.6s

~1.6s

~1.6s

#114

Qwen3.5-35B-A3B

35B (3B active)

INT4
Q8

188 tok/s

136 tok/s

~739 ms

~741 ms

#114

Ling 3.0 Flash

124B (5.1B active)

INT4

136 tok/s

~1.9s

#118

K2 Horizon 7B

7B

INT4
Q8
FP16

127 tok/s

78 tok/s

42 tok/s

~1.2s

~1.2s

~1.3s

#126

Qwen3 Next 80B A3B

80B (79B active)

INT4

11 tok/s

~14.6s

#128

NVIDIA Nemotron 3 Nano 30B-A3B

3.5B (30B active)

INT4
Q8
FP16

34 tok/s

20 tok/s

10 tok/s

~5.0s

~5.0s

~5.0s

Inference Capacity

Maximum model parameter class runnable by weight precision.

Context:

4-bit

Q4

70B+ 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

70B+ Models

LoRA

16-bit

70B Models

Full Parameter Training

7B-8B Models

Workload Recommendations

Guidance for optimal precision and operational ceilings on NVIDIA A16 (64GB).

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 800 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 178 tok/s on an 8B Q4 model and 21 tok/s on a 70B Q4 model.

Frequently Asked Questions