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NVIDIA H20 (96GB)

The NVIDIA H20 (96GB) features 96 GB of dedicated VRAM and 4000 GB/s memory bandwidth, supporting CUDA and TensorRT-LLM runtimes for full-precision and quantized inference.

Memory

96 GB

Usable Memory Ceiling

96 GB

Bandwidth

4000 GB/s

Max Inference Class

70B+ 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 14B (14B)Qwen3-30B-A3B (30B (3B active))Nemotron 3.5 Bolt 74B A9B (74B)GLM-5.3 Flash (313.3B (17.3B active))

Precision:

Best Models to Run on NVIDIA H20 (96GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA H20 (96GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#36

Qwen 3.8 27B

27B

INT4
Q8
FP16

158 tok/s

99 tok/s

54 tok/s

~2.3s

~2.3s

~2.3s

#42

Qwen 3.8 Flash

176B (6B active)

INT4

373 tok/s

~528 ms

#69

Sarvam-105B

106B (10.3B active)

INT4

294 tok/s

~1.2s

#80

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4
Q8
FP16

410 tok/s

334 tok/s

236 tok/s

~442 ms

~443 ms

~444 ms

#83

Ling 3.0 Flash VL

124B (5.5B active)

INT4

386 tok/s

~822 ms

#86

Qwen3.5-27B

27B

INT4
Q8
FP16

158 tok/s

99 tok/s

54 tok/s

~2.3s

~2.3s

~2.3s

#88

Qwen3.5-122B-A10B

122B (10B active)

INT4

249 tok/s

~1.2s

#93

Sarvam-30B

32B (2.4B active)

INT4
Q8
FP16

464 tok/s

404 tok/s

310 tok/s

~309 ms

~309 ms

~310 ms

#99

Muse Glimmer 30B

30B

INT4
Q8
FP16

146 tok/s

90 tok/s

49 tok/s

~2.5s

~2.5s

~2.5s

#102

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

197 tok/s

143 tok/s

88 tok/s

~1.2s

~1.2s

~1.2s

#103

Qwen3.6 35B A3B

35B (3B active)

INT4
Q8
FP16

442 tok/s

374 tok/s

277 tok/s

~360 ms

~361 ms

~362 ms

#105

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

18 tok/s

10 tok/s

5 tok/s

~24.6s

~24.6s

~24.7s

#110

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8
FP16

424 tok/s

347 tok/s

246 tok/s

~405 ms

~405 ms

~406 ms

#113

GLM-4.5-Air

106B (12B active)

INT4

108 tok/s

~1.3s

#117

Nemotron 3.5 Bolt 74B A9B

74B

INT4
Q8

69 tok/s

38 tok/s

~6.1s

~6.6s

#120

Qwen3.5-35B-A3B

35B (3B active)

INT4
Q8
FP16

442 tok/s

374 tok/s

277 tok/s

~360 ms

~361 ms

~362 ms

#121

Qwen3.5-9B

9B

INT4
Q8
FP16

319 tok/s

227 tok/s

138 tok/s

~768 ms

~769 ms

~772 ms

#122

Ling 3.0 Flash

124B (5.1B active)

INT4

397 tok/s

~790 ms

#127

K2 Horizon 7B

7B

INT4
Q8
FP16

360 tok/s

265 tok/s

168 tok/s

~601 ms

~602 ms

~605 ms

#130

Gemma 4 12B

11.95B

INT4
Q8
FP16

217 tok/s

161 tok/s

100 tok/s

~1.0s

~1.0s

~1.0s

Inference Capacity

Maximum model parameter class runnable by weight precision.

Context:

4-bit

Q4

70B+ Models

8-bit

Q8

70B Models

16-bit

FP16

32B 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 H20 (96GB).

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 4000 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 889 tok/s on an 8B Q4 model and 105 tok/s on a 70B Q4 model.

Frequently Asked Questions