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Can I run this LLM?

Can a NVIDIA RTX 3090 (24GB) run Phi-4 14B?

Phi · MIT · estimated for a 24.0 GB machine at 4K context.

Yes — it runs great.

Runs great

Best fit: Phi-4 14B at Q8_0 · 15.6 GB weights · 38–57 tok/s on a NVIDIA RTX 3090 (24GB).

Arena Text Overall · style-controlled

1,256Q8_0 untested

phi-4 · published configuration reference · Q8_0 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 Q8_0 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
17.2 GB
Weights + KV cache + runtime overhead
Estimated generation
38–57 tok/s
Estimated first token
~5.1s

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 NVIDIA RTX 3090 (24GB), estimated at 4K context. Weights and total memory are shown separately.
QuantWeightsTotal memorySpeedFit
Q2_K5.6 GB7.2 GB83–124 tok/sRuns great
Q3_K_M7.2 GB8.8 GB70–105 tok/sRuns great
Q4_K_M8.9 GB10.5 GB60–89 tok/sRuns great
Q5_K_M10.4 GB12.0 GB53–79 tok/sRuns great
Q6_K12.0 GB13.7 GB47–70 tok/sRuns great
Q8_015.6 GB17.2 GB38–57 tok/sRuns great
FP1629.3 GB31.0 GB— not residentWon't fit

Ran Phi-4 14B on a NVIDIA RTX 3090 (24GB)? Add your real numbers →