How much VRAM does Phi-4 14B need at Q4_K_M? Microsoft 16K native
Phi-4 14B is the perfect target for a single consumer GPU. It runs with full speed on a 16 GB RTX 4080 or RTX 5080, leaving plenty of headroom. You can also run it on a 12 GB card if you cap the context length slightly or use a lighter quantization option.
Phi-4 14B at Q4_K_M with its native 16K context needs about 12.3 GB of VRAM total. Microsoft built this dense model with 40 layers, a hidden size of 5120, and 10 key-value heads. Since this is a dense model rather than a mixture of experts, there's no offload option because every parameter activates for every token. This memory footprint makes it an exceptional choice for consumer graphics cards.
By TechCompare · Updated
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Estimated VRAM required
12.3 GB
14B params at Q4_K_M, 16,384 token context, batch 1, inference.
Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA or hybrid state estimates. Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.
Hardware that fits
Apple entries show installed unified memory separately from a model budget based on an 8 GB reserve. That budget is a planning heuristic, not a fixed hardware limit.
Just barely too small
How this is calculated
The 14B parameters require 7.8 GB of weights when quantized to Q4_K_M. The key-value cache consumes 3.4 GB at the full 16K context window with standard FP16 precision. General software and driver overhead adds about 1.1 GB, leading to the 12.3 GB total. It's extremely efficient compared to older models of similar size.
Verdict
The 12.3 GB total splits as 7.8 GB of Q4 weights, 3.4 GB of FP16 KV cache at the native 16K context (10 KV heads at head_dim 128 across 40 layers), and 1.1 GB of overhead. That lands cleanly on a 16 GB RTX 4080 or 5080 with comfortable buffer for additional context, batched serving, or the OS itself. A 12 GB card needs a nudge: capping context to 8K drops the cache to about 1.7 GB and the total to about 10.6 GB, which fits with room to spare. Phi-4 has no MoE offload option since every parameter activates per token, so the budget strategy is context capping and Q8 KV rather than expert offload. That dense activation is what makes the per-token cost predictable and the deployment simple.
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Frequently asked questions
Can I run Phi-4 14B on a 12 GB graphics card?
Does Phi-4 14B support longer context lengths?
How does Phi-4 14B compare to Llama 3.1 8B for self-hosting?
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