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NVIDIA A100 (40GB)

The NVIDIA A100 (40GB) features 40 GB of dedicated VRAM and 1555 GB/s memory bandwidth, supporting CUDA and TensorRT-LLM runtimes for full-precision and quantized inference.

Memory

40 GB

Usable Memory Ceiling

40 GB

Bandwidth

1555 GB/s

Max Inference Class

32B Models (Q4)

TDP

400W

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)Yi-9B (9B)DeepSeek-R1 32B (32B)Ling 3.0 Flash VL (124B (5.5B active))

Precision:

Best Models to Run on NVIDIA A100 (40GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA A100 (40GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#33

Qwen 3.8 27B

27B

INT4
Q8

72 tok/s

42 tok/s

~1.1s

~1.1s

#48

Agnes 3.0 Flash

33B

INT4
Q8

60 tok/s

33 tok/s

~1.3s

~1.4s

#72

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4
Q8

255 tok/s

178 tok/s

~214 ms

~234 ms

#83

Qwen3.5-27B

27B

INT4
Q8

72 tok/s

42 tok/s

~1.1s

~1.1s

#92

Sarvam-30B

32B (2.4B active)

INT4
Q8

314 tok/s

237 tok/s

~150 ms

~163 ms

#94

Qwen3.6 35B A3B

35B (3B active)

INT4
Q8

289 tok/s

210 tok/s

~175 ms

~190 ms

#96

Muse Glimmer 30B

30B

INT4
Q8

66 tok/s

36 tok/s

~1.2s

~1.3s

#100

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

93 tok/s

64 tok/s

35 tok/s

~572 ms

~577 ms

~639 ms

#101

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

7 tok/s

4 tok/s

2 tok/s

~11.8s

~11.9s

~13.2s

#108

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8

270 tok/s

198 tok/s

~196 ms

~197 ms

#109

Gemma 4 12B

11.95B

INT4
Q8
FP16

105 tok/s

73 tok/s

43 tok/s

~491 ms

~495 ms

~504 ms

#113

Qwen3.5-9B

9B

INT4
Q8
FP16

175 tok/s

111 tok/s

62 tok/s

~370 ms

~373 ms

~381 ms

#114

Qwen3.5-35B-A3B

35B (3B active)

INT4
Q8

289 tok/s

210 tok/s

~175 ms

~190 ms

#118

K2 Horizon 7B

7B

INT4
Q8
FP16

209 tok/s

136 tok/s

77 tok/s

~290 ms

~293 ms

~298 ms

#128

NVIDIA Nemotron 3 Nano 30B-A3B

3.5B (30B active)

INT4
Q8
FP16

63 tok/s

37 tok/s

20 tok/s

~1.2s

~1.2s

~1.2s

#129

Gemma 4 31B

30.7B

INT4
Q8

64 tok/s

36 tok/s

~1.2s

~1.3s

#131

Nemotron 3.5 Lightning

30B (3B active)

INT4
Q8

129 tok/s

113 tok/s

~175 ms

~191 ms

#132

Qwen3.5-4B

4B

INT4
Q8
FP16

292 tok/s

205 tok/s

124 tok/s

~171 ms

~173 ms

~176 ms

#133

Qwen3-30B-A3B

30B (3B active)

INT4
Q8

292 tok/s

212 tok/s

~171 ms

~187 ms

#136

GPT-OSS 20B

21B (3.6B active)

INT4
Q8

143 tok/s

125 tok/s

~187 ms

~188 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

3B Models

Workload Recommendations

Guidance for optimal precision and operational ceilings on NVIDIA A100 (40GB).

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 1555 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 346 tok/s on an 8B Q4 model and 41 tok/s on a 70B Q4 model.

Frequently Asked Questions