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NVIDIA B100 (192GB)

The NVIDIA B100 (192GB) features 192 GB of dedicated VRAM and 8000 GB/s memory bandwidth, supporting CUDA and TensorRT-LLM runtimes for full-precision and quantized inference.

Memory

192 GB

Usable Memory Ceiling

192 GB

Bandwidth

8000 GB/s

Max Inference Class

70B+ Models (Q4)

TDP

1000W

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)OLMo 3.1 32B Think (32B)GPT-OSS 120B (117B (5.1B active))DeepSeek V4.1 Flash (552B (16B active))

Precision:

Best Models to Run on NVIDIA B100 (192GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on NVIDIA B100 (192GB).

Sort by:

Context:

KV Cache:

GPUs:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#33

MiMo-V2.6-Flash

309B

INT4

33 tok/s

~2.4s

#35

DeepSeek-V4-Flash-Vision-Exp

290.9B (20.4B active)

INT4

308 tok/s

~145 ms

#36

Qwen 3.8 27B

27B

INT4
Q8
FP16

256 tok/s

172 tok/s

100 tok/s

~198 ms

~200 ms

~204 ms

#42

Qwen 3.8 Flash

176B (6B active)

INT4
Q8

495 tok/s

388 tok/s

~45 ms

~49 ms

#48

K2 Horizon 375B A23B

375B (23B active)

INT4

209 tok/s

~269 ms

#51

DeepSeek-V4-Flash

284B (13B active)

INT4

382 tok/s

~94 ms

#54

GLM-5.3 Flash

313.3B (17.3B active)

INT4

318 tok/s

~134 ms

#69

Sarvam-105B

106B (10.3B active)

INT4
Q8

408 tok/s

315 tok/s

~102 ms

~103 ms

#80

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4
Q8
FP16

508 tok/s

445 tok/s

348 tok/s

~41 ms

~42 ms

~42 ms

#83

Ling 3.0 Flash VL

124B (5.5B active)

INT4
Q8

489 tok/s

411 tok/s

~74 ms

~74 ms

#86

Qwen3.5-27B

27B

INT4
Q8
FP16

256 tok/s

172 tok/s

100 tok/s

~198 ms

~200 ms

~204 ms

#88

Qwen3.5-122B-A10B

122B (10B active)

INT4
Q8

362 tok/s

291 tok/s

~105 ms

~106 ms

#93

Sarvam-30B

32B (2.4B active)

INT4
Q8
FP16

547 tok/s

503 tok/s

423 tok/s

~30 ms

~30 ms

~30 ms

#99

Muse Glimmer 30B

30B

INT4
Q8
FP16

240 tok/s

159 tok/s

91 tok/s

~219 ms

~221 ms

~226 ms

#102

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

304 tok/s

236 tok/s

156 tok/s

~106 ms

~107 ms

~109 ms

#103

Qwen3.6 35B A3B

35B (3B active)

INT4
Q8
FP16

531 tok/s

479 tok/s

391 tok/s

~34 ms

~34 ms

~35 ms

#103

Qwen3 235B A22B Thinking

235B (22B active)

INT4

129 tok/s

~202 ms

#105

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

35 tok/s

20 tok/s

10 tok/s

~2.1s

~2.2s

~2.2s

#110

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8
FP16

518 tok/s

456 tok/s

359 tok/s

~38 ms

~38 ms

~39 ms

#113

GLM-4.5-Air

106B (12B active)

INT4
Q8

186 tok/s

167 tok/s

~117 ms

~118 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

70B 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 B100 (192GB).

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 8000 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 1778 tok/s on an 8B Q4 model and 211 tok/s on a 70B Q4 model.

Frequently Asked Questions