Can I Run Phi-4 Mini on NVIDIA GeForce RTX 5060?
Written by Jakub Rusinowski · Last updated July 12, 2026
Yes, comfortably — Phi-4 Mini (3.8B) at Q4_K_M needs 4.2 GB at 8K context (2.3 GB weights + 1.9 GB KV cache/overhead), leaving ~3.8 GB of the NVIDIA GeForce RTX 5060's 8 GB, at ~103 tok/s (est.), with room for up to 32K context.
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NVIDIA GeForce RTX 5060 Specs
| VRAM | 8 GB |
| Memory Bandwidth | 448 GB/s |
Phi-4 Mini (3.8B) on the NVIDIA GeForce RTX 5060: VRAM by quantization
| Quant | VRAM needed | Fits 8 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 9.5 GB | ✗ No | — | — |
| Q8_0 | 5.9 GB | ✓ Yes | 16K | — |
| Q6_K | 5 GB | ✓ Yes | 16K | — |
| Q5_K_M | 4.6 GB | ✓ Yes | 32K | — |
| Q4_K_M | 4.2 GB | ✓ Yes | 32K | — |
| Q3_K_M | 3.5 GB | ✓ Yes | 32K | — |
| Q2_K | 3.1 GB | ✓ Yes | 32K | — |
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 8 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 5060: 8K 4.2 GB · 32K 7.4 GB · 125K 19.9 GB ✗. Past 125K it no longer fits 8 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
No compatible GPU? Phi-4 Mini (3.8B) on 8 GB of system RAM, CPU only: runs, but it is tight.
Phi-4 Mini Sizes That Fit the NVIDIA GeForce RTX 5060 (at 8K context)
| Phi-4 Mini (3.8B) | Q4_K_M · 4.2 GB at 8K context · ~103 tok/s (est.) |
At 2 hrs/day, buying (~$299) beats renting at $0.34/hr after about 15 months.
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FAQ
Can I run Phi-4 Mini on the NVIDIA GeForce RTX 5060?
Yes, comfortably — Phi-4 Mini (3.8B) at Q4_K_M needs 4.2 GB at 8K context (2.3 GB weights + 1.9 GB KV cache/overhead), leaving ~3.8 GB of the NVIDIA GeForce RTX 5060's 8 GB, at ~103 tok/s (est.), with room for up to 32K context.
What's the best Phi-4 Mini size for the NVIDIA GeForce RTX 5060?
Phi-4 Mini (3.8B) at Q4_K_M quantization needs 4.2 GB at 8K context (2.3 GB weights + 1.9 GB KV cache/overhead), estimated ~103 tokens/sec, up to 32K context.
Phi-4 Mini on Other GPUs
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VRAM Tier
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