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Can I run this LLM?

Can a Mac Studio M3 Ultra (96GB) run Phi-4 14B?

Phi · MIT · estimated for a 96.0 GB machine at 4K context.

Yes — it runs great.

Runs great

Best fit: Phi-4 14B at FP16 · 29.3 GB weights · 17–25 tok/s on a Mac Studio M3 Ultra (96GB).

Arena Text Overall · style-controlled

1,256FP16 untested

phi-4 · published configuration reference · FP16 not tested

Arena Text Overall (style-controlled), exact published entry phi-4, 2026-10-08: reference rating 1,256.3. Published confidence bounds 1,251.7–1,260.9. 24,126 votes. Published phi-4 configuration reference. Reasoning mode is not disclosed by the Arena dataset. Tested precision, runtime and sampling settings are not disclosed in this dataset. Selected FP16 has not been measured by this Arena entry. No quantization quality adjustment is applied.

Snapshot published 2026-10-08 · snapshot imported 2026-10-11 UTC · validated last-good data · manual refresh

Arena leaderboard dataset · Arena / lmarena-ai, Arena Leaderboard Dataset (CC BY 4.0). Selected Text Overall style-controlled rows; field names adapted, ratings unchanged. · dataset CC BY 4.0 · revision e16f90232986

Total memory at 4K context
31.0 GB
Weights + KV cache + runtime overhead
Estimated generation
17–25 tok/s
Estimated first token
~6.5s

Every quantization on this machine

On small screens, each quant lists every metric below its name. On wider screens, scroll sideways for total memory, speed and fit. Keyboard: focus the table, then use the arrow keys. Quant names stay visible.

Phi-4 14B quantizations on Mac Studio M3 Ultra (96GB), estimated at 4K context. Weights and total memory are shown separately.
QuantWeightsTotal memorySpeedFit
Q2_K5.6 GB7.2 GB57–86 tok/sRuns great
Q3_K_M7.2 GB8.8 GB49–74 tok/sRuns great
Q4_K_M8.9 GB10.5 GB43–64 tok/sRuns great
Q5_K_M10.4 GB12.0 GB38–57 tok/sRuns great
Q6_K12.0 GB13.7 GB34–52 tok/sRuns great
Q8_015.6 GB17.2 GB28–42 tok/sRuns great
FP1629.3 GB31.0 GB17–25 tok/sRuns great

Ran Phi-4 14B on a Mac Studio M3 Ultra (96GB)? Add your real numbers →