Microsoft on NVIDIA Ampere

Can I run Phi-4 14B on an A100 80GB?

Yes. Phi-4 14B at Q4_K_M uses 10.8 GB of the 74.4 GB available on a A100 80GB, and runs at about 135 tokens per second. You can push the context to 16K.

Runs comfortably

10.8 GB of 74.4 GB · 14%
074 GB
Weights 8.4 GB
KV cache 1.6 GB
Runtime overhead 0.8 GB

Phi-4 14B at Q4_K_M leaves 63.6 GB spare on a A100 80GB. There is room to raise the context length or move up a quantisation level.

Generation135tok/s
Prompt processing4457tok/s
Max context16Ktokens
KV per 1K tokens0GB

Every quantisation of Phi-4 14B on a A100 80GB

Highlighted row is the highest quality that still fits at 8K context.

QuantisationbpwWeights TotalVerdictMax ctxtok/s
FP16 / BF16 16.00 27.4 GB 29.8 GB Runs comfortably 16K 44.8
INT8 / W8A8 8.50 14.3 GB 16.7 GB Runs comfortably 16K 82.9
Q8_0 (GGUF) 8.50 14.3 GB 16.7 GB Runs comfortably 16K 82.9
FP8 (E4M3) 8.00 13.7 GB 16.1 GB Runs comfortably 16K 86.4
Q6_K 6.56 11.2 GB 13.6 GB Runs comfortably 16K 104
Q5_K_M 5.67 9.7 GB 12.1 GB Runs comfortably 16K 118
Q5_K_S 5.52 9.4 GB 11.8 GB Runs comfortably 16K 121
AWQ 4-bit 4.25 8.7 GB 11.1 GB Runs comfortably 16K 131
GPTQ 4-bit 4.25 8.7 GB 11.1 GB Runs comfortably 16K 131
MXFP4 4.25 8.7 GB 11.1 GB Runs comfortably 16K 131
Q4_K_M 4.85 8.4 GB 10.8 GB Runs comfortably 16K 135
Q4_K_S 4.58 7.9 GB 10.3 GB Runs comfortably 16K 141
Q4_0 4.55 7.9 GB 10.3 GB Runs comfortably 16K 142
IQ4_XS 4.25 7.4 GB 9.8 GB Runs comfortably 16K 150
Q3_K_M 3.91 6.9 GB 9.3 GB Runs comfortably 16K 160
IQ3_M 3.70 6.5 GB 8.9 GB Runs comfortably 16K 167
IQ3_XXS 3.06 5.5 GB 7.9 GB Runs comfortably 16K 193
Q2_K 2.63 4.8 GB 7.2 GB Runs comfortably 16K 215
IQ2_XXS 2.06 3.9 GB 6.3 GB Runs comfortably 16K 254
IQ1_M 1.75 3.4 GB 5.8 GB Runs comfortably 16K 282

Also worth checking

Phi-4 14B on every GPU
Full hardware tableEverything that runs on a A100 80GB
Full model tableWhy the KV cache is the part that bites
Guide