Can I Run Phi-4 Family on NVIDIA GeForce RTX 4080?
Written by Jakub Rusinowski · Last updated July 12, 2026
Yes, comfortably — Phi-4 (14B) at Q4_K_M needs 10.9 GB at 8K context (8.5 GB weights + 2.4 GB KV cache/overhead), leaving ~5.1 GB of the NVIDIA GeForce RTX 4080's 16 GB, at ~62 tok/s (est.), with room for up to 16K context.
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NVIDIA GeForce RTX 4080 Specs
| VRAM | 16 GB |
| Memory Bandwidth | 717 GB/s |
Phi-4 (14B) on the NVIDIA GeForce RTX 4080: VRAM by quantization
| Quant | VRAM needed | Fits 16 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 30.5 GB | ✗ No | — | — |
| Q8_0 | 17.4 GB | ✗ No | — | Build for this |
| Q6_K | 14 GB | ✓ Yes | 16K | — |
| Q5_K_M | 12.4 GB | ✓ Yes | 16K | Build for this |
| Q4_K_M | 10.9 GB | ✓ Yes | 16K | Build for this |
| Q3_K_M | 8.4 GB | ✓ Yes | 16K | — |
| Q2_K | 7.1 GB | ✓ Yes | 16K | — |
Assumes an 8K-token context with an f16 KV cache. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 16 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
Context costs VRAM too. At Q4_K_M on the NVIDIA GeForce RTX 4080: 8K 10.9 GB · 16K 12.6 GB. Every window this model publishes still fits 16 GB.
No compatible GPU? Phi-4 (14B) on 16 GB of system RAM, CPU only: runs, but it is tight.
Phi-4 Family Sizes That Fit the NVIDIA GeForce RTX 4080 (at 8K context)
| Phi-4 (14B) | Q4_K_M · 10.9 GB at 8K context · ~62 tok/s (est.) |
At 2 hrs/day, buying (~$1,199) beats renting at $0.34/hr after about 4.9 years.
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FAQ
Does the NVIDIA GeForce RTX 4080 have enough VRAM for Phi-4 Family?
Yes, comfortably — Phi-4 (14B) at Q4_K_M needs 10.9 GB at 8K context (8.5 GB weights + 2.4 GB KV cache/overhead), leaving ~5.1 GB of the NVIDIA GeForce RTX 4080's 16 GB, at ~62 tok/s (est.), with room for up to 16K context.
Which quantization of Phi-4 Family should I use on the NVIDIA GeForce RTX 4080?
Phi-4 (14B) at Q4_K_M quantization needs 10.9 GB at 8K context (8.5 GB weights + 2.4 GB KV cache/overhead), estimated ~62 tokens/sec, up to 16K context.
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VRAM Tier
Troubleshooting
Buying Guide
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