Microsoft's Phi-4 is a compact 14B that runs on a mid-range card — but at 16K context and a late-2024 release, it shows its age next to newer 14B-class models. The VRAM math and an honest verdict.
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Phi-4 is worth a clear-eyed guide because its computed page still draws searches, and it's a genuinely useful small model — but it's also the oldest model in this batch, and honesty about that matters more than hype. Here's what it is, what it needs, and when a newer model is the better call.
What Phi-4 actually is
From Microsoft's model card (read 26 Sep 2026): a 14B-parameter dense model, MIT licensed (genuinely permissive), with a 16K-token context. It was released in December 2024. Its config.json shows a straightforward architecture (40 layers, full attention throughout), which is part of why it's easy to run — and part of why it's dated.
The dated part, said plainly: 16K context is small in 2026, when comparable models offer 128K or 256K. And because Phi-4 uses full attention on every layer (no sliding-window or linear-attention efficiency), its KV cache is heavier — our engine counts about 3 GB of KV at 16K context, several times what a similarly sized Gemma or Qwen uses. Neither is a flaw exactly; it's the shape of a late-2024 design.
What Phi-4 was known for is punching above its size on reasoning and maths for a 14B — Microsoft's stated goal for the Phi line was small models trained on carefully curated and synthetic data rather than sheer scale. In practice that made it a popular pick for people who wanted a compact, capable assistant on a mid-range card, and for that narrow brief it's still fine. The question in 2026 isn't whether it works; it's whether a newer model does the same job with fewer compromises.
How much VRAM it needs
Computed by our VRAM engine at 16K context (full table on cost to run Phi-4):
Phi-4 VRAM by quant (computed, 16K context)
Q4_K_M
Total VRAM
~12.2 GB
Q6_K
Total VRAM
~15.1 GB
Q8_0
Total VRAM
~18.4 GB
BF16 (full)
Total VRAM
~31 GB
Total VRAM
Q4_K_M
~12.2 GB
Q6_K
~15.1 GB
Q8_0
~18.4 GB
BF16 (full)
~31 GB
At ~12.2 GB, Q4_K_M is a tight fit for a 12GB card and comfortable on 16GB. Note that the ~3 GB KV cache is a bigger share of the total than you'd see on a newer small model, so a 16GB card is the safer choice if you want to use the full 16K context.
A note on the numbers: that KV cache is the tell. On Gemma 4 or Qwen3.5 the same 16K context would cost a fraction of it, because those models use sliding-window or linear attention; Phi-4's full attention pays the full price. It isn't a dealbreaker on a 16GB card, but it's the clearest reason a newer small model gives you more usable room. For quantisation, Q4_K_M is the sensible default, and because the model is only 14B the quality gap up to Q8 is small — a 16GB card can run Q8 comfortably if you want it. In practical terms, 16K is enough for a chat or a single-file coding task, but it fills up fast if you paste in a long document or a whole codebase, which is exactly the workload newer long-context models were built for.
Which GPU runs it
A 12GB card runs Q4 tightly; a 16GB card (an RTX 4060 Ti 16GB or similar) is the comfortable pick and lets you run Q6/Q8. See best GPU for Phi-4 for the ranked options.
Referral link
Rent a 16GB card for Phi-4
A 16GB RTX A4000 was listing from about $0.072/hr on Vast.ai's spot market (26 Sep 2026) — plenty for Phi-4 at Q8. Prices move; check the live figure before you rent.
Referral link — we may earn a commission at no cost to you. Prices on our compare page are the provider's live figures, cheapest first; this never changes the ranking.
How to run it
ollama run phi4:14b # the bare `phi4` tag resolves here too
Phi-4 has a single size on Ollama, so phi4 and phi4:14b pull the same model. For llama.cpp, use a Phi-4 GGUF with llama-server.
Who should skip it (and what to run instead)
Skip Phi-4 if you need long context — its 16K ceiling is the clearest sign of its age, and Gemma 4 12B or Qwen3.5 9B give a 256K context for a similar or smaller VRAM footprint, with newer training behind them. Reach for Phi-4 specifically if you want a small, permissively-licensed (MIT) reasoning-leaning model and 16K context is enough for your task — it's still a tidy, dependable model at that size.
Phi-4: common questions
How much VRAM does Phi-4 need?
About 12.2 GB at Q4_K_M (computed by our VRAM engine, 16K context) — a tight fit for a 12GB card, comfortable on 16GB.
Is Phi-4 still worth running in 2026?
For a compact, MIT-licensed model where 16K context is enough, yes. But its late-2024 design shows: newer 12–14B models like Gemma 4 12B or Qwen3.5 9B offer far larger context for a similar footprint.
Why is Phi-4's memory use higher than a similar-size Qwen?
It uses full attention on every layer, so its KV cache (~3 GB at 16K) is larger than models that use sliding-window or linear attention.
What licence is Phi-4?
MIT — genuinely permissive, including for commercial use, per the model card.
For newer small models with much larger context, compare Gemma 4 12B and Qwen3.5 9B; for the hardware picture, best GPU for local AI. Phi-4 isn't the model to build a fresh 2026 setup around, but if you already have it, or you specifically want a small, permissively-licensed, dependable option, it still earns a place on the shortlist.
