How fast will Qwen 3.6/3.8 27B feel on the H200 141GB (cloud)?

Qwen 3.6/3.8 27B on the H200 141GB (cloud)

Qwen 3.6/3.8 27B is a 27.78B-parameter model. At Q4_K_M it needs about 17.6 GB of memory. The H200 141GB (cloud) has 131 GB usable, so it fits entirely in memory. Expect about 115 tokens/sec (42–188), with the first token after 0.3 s for a short prompt. This estimate is interpolated from benchmarks of similar setups.

Speed by quantization

Quantization	File size	Memory	Generation	First token	Source
Q4_K_M	16.8 GB	Fits	115 tok/s	0.3 s	Derived
Q8_0	28.6 GB	Fits	68 tok/s	0.3 s	Derived
FP16	53.8 GB	Fits	36 tok/s	0.3 s	Derived

Qwen 3.6/3.8 27B on other hardware

H100 80GB (cloud) · 80 tok/s at Q4_K_M
A100 80GB (cloud) · 49 tok/s at Q4_K_M
NVIDIA RTX 5090 32GB · 43 tok/s at Q4_K_M
NVIDIA RTX PRO 6000 96GB · 43 tok/s at Q4_K_M
AMD RX 7900 XTX 24GB · 36 tok/s at Q4_K_M
Mac Studio M5 Ultra 512GB · 34 tok/s at Q4_K_M
AMD RX 7900 XT 20GB · 30 tok/s at Q4_K_M
Mac Studio M3 Ultra 512GB · 25 tok/s at Q4_K_M
NVIDIA RTX 4090 24GB · 24 tok/s at Q4_K_M
AMD Radeon AI PRO R9700 32GB · 24 tok/s at Q4_K_M
NVIDIA RTX 3090 24GB · 22 tok/s at Q4_K_M
MacBook Pro M5 Max 128GB · 19 tok/s at Q4_K_M
Mac Studio M4 Max 128GB · 17 tok/s at Q4_K_M
AMD RX 9070 XT 16GB · 14 tok/s at Q4_K_M
NVIDIA RTX 5080 16GB · 13 tok/s at Q4_K_M
MacBook M3 Max 48GB · 12 tok/s at Q4_K_M
NVIDIA RTX 5070 Ti 16GB · 12 tok/s at Q4_K_M
NVIDIA RTX 4070 Ti Super 16GB · 10 tok/s at Q4_K_M
AMD Ryzen AI Max+ 395 128GB (Strix Halo) · 9.5 tok/s at Q4_K_M
MacBook Pro M5 Pro 48GB · 9.5 tok/s at Q4_K_M
Mac Mini M4 Pro 64GB · 8.4 tok/s at Q4_K_M
MacBook M4 Pro 24GB · 8.4 tok/s at Q4_K_M
NVIDIA RTX 5060 Ti 16GB · 8.2 tok/s at Q4_K_M
Intel Arc Pro B70 32GB · 7.5 tok/s at Q4_K_M
NVIDIA DGX Spark 128GB · 6.5 tok/s at Q4_K_M
NVIDIA RTX 4070 12GB · 6.5 tok/s at Q4_K_M
NVIDIA RTX 3060 12GB · 5.7 tok/s at Q4_K_M
CPU-only (Ryzen 9 9950X, 96GB DDR5) · 3.6 tok/s at Q4_K_M
MacBook M2 Pro 16GB · won't run

Other models on the H200 141GB (cloud)

GLM-5.3 Flash · 287 tok/s at UD-IQ3_XXS
GPT-OSS 20B · 306 tok/s at MXFP4
Gemma 4 26B A4B · 775 tok/s at Q4_K_M
Qwen 3.5/3.6 35B A3B · 1048 tok/s at Q4_K_M
Ornith 1.5 35B A3B · 824 tok/s at Q4_K_M
DeepSeek V4 Flash · 353 tok/s at UD-Q3_K_XL
GPT-OSS 120B · 196 tok/s at MXFP4
Gemma 4 31B · 106 tok/s at Q4_K_M
Llama 3.1 8B · 393 tok/s at Q4_K_M
Qwen 3.5 9B · 263 tok/s at Q4_K_M
DeepSeek R1 Distill 14B · 215 tok/s at Q4_K_M
Ornith 1.5 9B · 258 tok/s at Q4_K_M
Muse Glimmer 30B · 75 tok/s at Q4_K_M
Qwen3 30B A3B · 744 tok/s at Q4_K_M

All models on the H200 141GB (cloud) · Qwen 3.6/3.8 27B on all hardware

Estimates for a short prompt on a single device. Measured numbers come from community benchmarks (r/LocalLLaMA, localscore.ai and more); others are derived from similar setups or projected from specs. Open source on GitHub.