Can I Run Microsoft Phi-4 14B on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Microsoft Phi-4 14B (14.7B) on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (24 GB).
✓ Fits Comfortably in VRAM
Local Hardware Configurations & Pricing
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
NVIDIA GeForce RTX 5080 (16GB GDDR7)
Blazing fast 8B–14B coding models with FP4/Q6_K precision
Cloud Instance Options for Microsoft Phi-4 14B
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 29.4 GB | 3.36 GB | 34.54 GB (144%) | 0 t/s | OOM |
| Q8_08.5 bpw | 15.58 GB | 3.36 GB | 20.24 GB (84%) | ~42 t/s | FITS |
| Q6_K6.56 bpw | 12.05 GB | 3.36 GB | 16.58 GB (69%) | ~53 t/s | FITS |
| Q5_K_M5.68 bpw | 10.44 GB | 3.36 GB | 14.92 GB (62%) | ~61 t/s | FITS |
| Q4_K_M4.8 bpw | 8.82 GB | 3.36 GB | 13.24 GB (55%) | ~71 t/s | FITS |
| FP44.25 bpw | 7.79 GB | 3.36 GB | 12.17 GB (51%) | ~79 t/s | FITS |
| IQ3_XS3.3 bpw | 6.03 GB | 3.36 GB | 10.35 GB (43%) | ~100 t/s | FITS |
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Frequently Asked Questions: Microsoft Phi-4 14B on NVIDIA GeForce RTX 4090 (24GB GDDR6X)
How much VRAM does Microsoft Phi-4 14B need on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Microsoft Phi-4 14B requires 13.24 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Microsoft Phi-4 14B achieve on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?
Based on NVIDIA GeForce RTX 4090 (24GB GDDR6X)'s 1008 GB/s memory bandwidth, expected decode speed is approximately 71 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.