ApX logoApX logo

NVIDIA H100 NVL (94GB)

The NVIDIA H100 NVL (94GB) features 94 GB of dedicated VRAM and 3938 GB/s memory bandwidth, supporting CUDA and TensorRT-LLM runtimes for full-precision and quantized inference.

Memory

94 GB

Usable Memory Ceiling

94 GB

Bandwidth

3938 GB/s

Max Inference Class

70B+ Models (Q4)

TDP

700W

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)Gemma 4 31B (30.7B)AliceAI Foundation 80B A3B Base (80B (3B active))GLM-5.3 Flash (313.3B (17.3B active))

Precision:

Best Models to Run on NVIDIA H100 NVL (94GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA H100 NVL (94GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#33

Qwen 3.8 27B

27B

INT4
Q8
FP16

157 tok/s

97 tok/s

53 tok/s

~408 ms

~412 ms

~420 ms

#41

Qwen 3.8 Flash

176B (6B active)

INT4

351 tok/s

~106 ms

#48

Agnes 3.0 Flash

33B

INT4
Q8
FP16

134 tok/s

82 tok/s

44 tok/s

~495 ms

~500 ms

~511 ms

#61

Sarvam-105B

106B (10.3B active)

INT4

292 tok/s

~210 ms

#72

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4
Q8
FP16

407 tok/s

332 tok/s

233 tok/s

~82 ms

~83 ms

~84 ms

#75

Ling 3.0 Flash VL

124B (5.5B active)

INT4

384 tok/s

~150 ms

#83

Qwen3.5-27B

27B

INT4
Q8
FP16

157 tok/s

97 tok/s

53 tok/s

~408 ms

~412 ms

~420 ms

#84

Qwen3.5-122B-A10B

122B (10B active)

INT4

246 tok/s

~215 ms

#92

Sarvam-30B

32B (2.4B active)

INT4
Q8
FP16

462 tok/s

401 tok/s

307 tok/s

~58 ms

~59 ms

~59 ms

#94

Qwen3.6 35B A3B

35B (3B active)

INT4
Q8
FP16

439 tok/s

372 tok/s

275 tok/s

~68 ms

~68 ms

~69 ms

#96

Muse Glimmer 30B

30B

INT4
Q8
FP16

145 tok/s

89 tok/s

48 tok/s

~452 ms

~456 ms

~466 ms

#100

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

195 tok/s

142 tok/s

87 tok/s

~217 ms

~219 ms

~223 ms

#101

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

18 tok/s

10 tok/s

5 tok/s

~4.4s

~4.5s

~4.6s

#106

GLM-4.5-Air

106B (12B active)

INT4

107 tok/s

~239 ms

#108

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8
FP16

421 tok/s

344 tok/s

243 tok/s

~76 ms

~76 ms

~77 ms

#109

Gemma 4 12B

11.95B

INT4
Q8
FP16

215 tok/s

159 tok/s

99 tok/s

~186 ms

~188 ms

~192 ms

#113

Qwen3.5-9B

9B

INT4
Q8
FP16

316 tok/s

224 tok/s

137 tok/s

~141 ms

~142 ms

~145 ms

#114

Qwen3.5-35B-A3B

35B (3B active)

INT4
Q8
FP16

439 tok/s

372 tok/s

275 tok/s

~68 ms

~68 ms

~69 ms

#114

Ling 3.0 Flash

124B (5.1B active)

INT4

394 tok/s

~144 ms

#118

K2 Horizon 7B

7B

INT4
Q8
FP16

357 tok/s

263 tok/s

166 tok/s

~111 ms

~112 ms

~114 ms

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 H100 NVL (94GB).

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 3938 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 875 tok/s on an 8B Q4 model and 104 tok/s on a 70B Q4 model.

Frequently Asked Questions