How fast will DeepSeek R1 Distill 14B feel on the A100 80GB (cloud)?

DeepSeek R1 Distill 14B on the A100 80GB (cloud)

DeepSeek R1 Distill 14B is a 14.77B-parameter model. At Q4_K_M it needs about 9.7 GB of memory. The A100 80GB (cloud) has 74 GB usable, so it fits entirely in memory. Expect about 69 tokens/sec (50–87), with the first token after 0.3 s for a short prompt. This estimate is based on community benchmarks of this exact setup.

Speed by quantization

Quantization	File size	Memory	Generation	First token	Source
Q4_K_M	9.0 GB	Fits	69 tok/s	0.3 s	Measured
Q8_0	15.7 GB	Fits	52 tok/s	0.3 s	Derived
FP16	29.6 GB	Fits	28 tok/s	0.3 s	Derived

DeepSeek R1 Distill 14B on other hardware

H200 141GB (cloud) · 215 tok/s at Q4_K_M
H100 80GB (cloud) · 75 tok/s at Q4_K_M
Mac Studio M5 Ultra 512GB · 63 tok/s at Q4_K_M
NVIDIA RTX 3090 24GB · 62 tok/s at Q4_K_M
AMD RX 7900 XT 20GB · 56 tok/s at Q4_K_M
NVIDIA RTX 4090 24GB · 54 tok/s at Q4_K_M
NVIDIA RTX 5090 32GB · 53 tok/s at Q4_K_M
NVIDIA RTX PRO 6000 96GB · 47 tok/s at Q4_K_M
AMD RX 9070 XT 16GB · 45 tok/s at Q4_K_M
AMD Radeon AI PRO R9700 32GB · 45 tok/s at Q4_K_M
NVIDIA RTX 4070 Ti Super 16GB · 38 tok/s at Q4_K_M
NVIDIA RTX 4070 12GB · 37 tok/s at Q4_K_M
MacBook Pro M5 Max 128GB · 37 tok/s at Q4_K_M
Mac Studio M3 Ultra 512GB · 36 tok/s at Q4_K_M
NVIDIA RTX 5070 Ti 16GB · 32 tok/s at Q4_K_M
Mac Studio M4 Max 128GB · 29 tok/s at Q4_K_M
NVIDIA RTX 5060 Ti 16GB · 29 tok/s at Q4_K_M
AMD RX 7900 XTX 24GB · 26 tok/s at Q4_K_M
NVIDIA RTX 5080 16GB · 25 tok/s at Q4_K_M
MacBook M3 Max 48GB · 24 tok/s at Q4_K_M
MacBook Pro M5 Pro 48GB · 24 tok/s at Q4_K_M
NVIDIA DGX Spark 128GB · 19 tok/s at Q4_K_M
Mac Mini M4 Pro 64GB · 18 tok/s at Q4_K_M
MacBook M4 Pro 24GB · 18 tok/s at Q4_K_M
AMD Ryzen AI Max+ 395 128GB (Strix Halo) · 18 tok/s at Q4_K_M
Intel Arc Pro B70 32GB · 14 tok/s at Q4_K_M
MacBook M2 Pro 16GB · 13 tok/s at Q4_K_M
NVIDIA RTX 3060 12GB · 12 tok/s at Q4_K_M
CPU-only (Ryzen 9 9950X, 96GB DDR5) · 6.6 tok/s at Q4_K_M

Other models on the A100 80GB (cloud)

Qwen 3.6/3.8 27B · 49 tok/s at Q4_K_M
GLM-5.3 Flash · won't run
GPT-OSS 20B · 394 tok/s at MXFP4
Gemma 4 26B A4B · 329 tok/s at Q4_K_M
Qwen 3.5/3.6 35B A3B · 445 tok/s at Q4_K_M
Ornith 1.5 35B A3B · 350 tok/s at Q4_K_M
DeepSeek V4 Flash · won't run
GPT-OSS 120B · 297 tok/s at MXFP4
Gemma 4 31B · 45 tok/s at Q4_K_M
Llama 3.1 8B · 114 tok/s at Q4_K_M
Qwen 3.5 9B · 112 tok/s at Q4_K_M
Ornith 1.5 9B · 110 tok/s at Q4_K_M
Muse Glimmer 30B · 32 tok/s at Q4_K_M
Qwen3 30B A3B · 316 tok/s at Q4_K_M

All models on the A100 80GB (cloud) · DeepSeek R1 Distill 14B 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.