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NVIDIA H100 (80GB)

The NVIDIA H100 (80GB) features 80 GB of dedicated VRAM and 3350 GB/s memory bandwidth, supporting CUDA and TensorRT-LLM runtimes for full-precision and quantized inference.

Memory

80 GB

Usable Memory Ceiling

80 GB

Bandwidth

3350 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)Nemotron 3.5 Lightning 30B A3B (30B (3B active))Llama 3.3 70B (70B)Qwen3-235B-A22B (235B (22B active))

Precision:

Best Models to Run on NVIDIA H100 (80GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA H100 (80GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#33

Qwen 3.8 27B

27B

INT4
Q8
FP16

138 tok/s

85 tok/s

46 tok/s

~347 ms

~351 ms

~362 ms

#48

Agnes 3.0 Flash

33B

INT4
Q8
FP16

118 tok/s

71 tok/s

36 tok/s

~421 ms

~427 ms

~476 ms

#61

Sarvam-105B

106B (10.3B active)

INT4

266 tok/s

~178 ms

#72

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4
Q8
FP16

381 tok/s

305 tok/s

199 tok/s

~70 ms

~71 ms

~79 ms

#75

Ling 3.0 Flash VL

124B (5.5B active)

INT4

339 tok/s

~138 ms

#83

Qwen3.5-27B

27B

INT4
Q8
FP16

138 tok/s

85 tok/s

46 tok/s

~347 ms

~351 ms

~362 ms

#84

Qwen3.5-122B-A10B

122B (10B active)

INT4

211 tok/s

~198 ms

#92

Sarvam-30B

32B (2.4B active)

INT4
Q8
FP16

438 tok/s

375 tok/s

267 tok/s

~50 ms

~50 ms

~55 ms

#94

Qwen3.6 35B A3B

35B (3B active)

INT4
Q8
FP16

415 tok/s

345 tok/s

236 tok/s

~58 ms

~58 ms

~64 ms

#96

Muse Glimmer 30B

30B

INT4
Q8
FP16

127 tok/s

77 tok/s

41 tok/s

~384 ms

~389 ms

~401 ms

#100

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

173 tok/s

124 tok/s

75 tok/s

~185 ms

~187 ms

~193 ms

#101

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

15 tok/s

8 tok/s

4 tok/s

~3.8s

~3.8s

~3.9s

#106

GLM-4.5-Air

106B (12B active)

INT4

88 tok/s

~223 ms

#108

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8
FP16

396 tok/s

317 tok/s

219 tok/s

~65 ms

~65 ms

~67 ms

#109

Gemma 4 12B

11.95B

INT4
Q8
FP16

192 tok/s

140 tok/s

86 tok/s

~159 ms

~161 ms

~165 ms

#113

Qwen3.5-9B

9B

INT4
Q8
FP16

290 tok/s

201 tok/s

120 tok/s

~120 ms

~122 ms

~125 ms

#114

Qwen3.5-35B-A3B

35B (3B active)

INT4
Q8
FP16

415 tok/s

345 tok/s

236 tok/s

~58 ms

~58 ms

~64 ms

#114

Ling 3.0 Flash

124B (5.1B active)

INT4

350 tok/s

~133 ms

#118

K2 Horizon 7B

7B

INT4
Q8
FP16

330 tok/s

237 tok/s

147 tok/s

~95 ms

~96 ms

~99 ms

#126

Qwen3 Next 80B A3B

80B (79B active)

INT4

44 tok/s

~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 H100 (80GB).

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 3350 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 744 tok/s on an 8B Q4 model and 88 tok/s on a 70B Q4 model.

Frequently Asked Questions