Phi-4 14B · 11 GPUs measured first-party · llama.cpp Q4_K_M · Updated October 2026

What GPU Do You Need for Phi-4 14B?

Phi-4 14B is the full-size version of Microsoft's synthetic-data recipe, and the fastest 14B in our database at 177 tok/s (B300), with a ~10GB measured floor. We benchmarked it on 11 GPUs with the same pinned llama.cpp harness as every model on this site.

Benchmarked weights: bartowski/phi-4-GGUF

Fastest we measured

NVIDIA B300

177.4 tok/s on Phi-4 14B, the ceiling. Measured on our bench. 288GB of VRAM, $40,000 at launch.

Pros

• 177.4 tok/s on Phi-4 14B
• 288GB, clears the Phi-4 14B floor
• Rentable by the hour rather than bought

Cons

• 1400W board rating
• Datacenter or workstation hardware, not a retail purchase

Cheapest card that runs it

NVIDIA RTX PRO 6000 Blackwell Workstation Edition

142.7 tok/s on Phi-4 14B, lowest launch price that still fits. Measured on our bench. 96GB of VRAM, $8,565 at launch.

Pros

• 142.7 tok/s on Phi-4 14B
• 96GB, clears the Phi-4 14B floor
• Rentable by the hour rather than bought

Cons

• 600W board rating
• Datacenter or workstation hardware, not a retail purchase

Honest positioning: good, not the pick. Phi-4 14B benchmarks beautifully, it out-runs both DeepSeek-R1 14B (156 tok/s) and Qwen3 14B (165 tok/s) at the same weight. But speed isn't why you choose a mid-size local model, and here's our candid ranking: most people running 10-20GB-class models locally are ultimately doing coding and technical work, and in that lane the DeepSeek distill's reasoning, the Dolphin 24Bs' depth, and Mistral's coder models all earn their seats ahead of it. Phi-4's strength is polished general-knowledge answering, real, but a narrower reason to allocate your VRAM.

Where it does fit. If your workload is genuinely general, explanation, writing, Q&A, the speed advantage is real value: 177 tok/s peak and 168 on the H200 at 203W make it the snappiest 14B experience we've measured, and the ~10GB floor keeps 12GB cards in play. As a fast direct-answer counterweight in a panel of slower reasoning models, it also has a legitimate niche: it answers while the others think.

How it compares. H100 80GB HBM3: Phi-4 14B 165.2 tok/s, GLM-4.7-Flash-REAP-23B-A3B 168.4, gemma-2-9b 174.7 (9B), NemoMix-Unleashed-12B 177.3, Mistral-Nemo-Instruct-2407 177.4. All 4 beat Phi-4 14B here. GLM-4.7-Flash-REAP-23B-A3B is a mixture-of-experts, so per token it computes only a slice of its size.

Cost on a rented GPU. 1M generated tokens of Phi-4 14B: $1.33 on a A100 40GB SXM4 ($0.47/hr, 2.8 hours), $10.87 on a B300 ($6.94/hr, 94 min, 8.2x the cost).

Speed tiers for Phi-4 14B. 30+ tok/s: 9 (B300, H200, H100 80GB HBM3); 10-30 tok/s: 2 (L4, T4). 30 tok/s is roughly where replies outpace reading.

Reading your prompt. Before Phi-4 14B writes anything it reads the input: 7777.4 tok/s on the RTX PRO 6000 Blackwell Workstation Edition (0.5s for a 4,000-token prompt), 662.0 on the T4 (6.0s). Long documents and big code files feel this number more than the generation speed.

VRAM for Phi-4 14B. Measured peak 9.0GB, so 12GB is the smallest common card size; smallest card it ran on: T4 (16GB). With long context: Q4_K_M 11GB (tested), Q2_K 7GB, Q3_K_M 9GB, Q5_K_M 13GB, Q6_K 15GB.

Power on Phi-4 14B. Most efficient: H200, 203W, 0.34 kWh per 1M generated tokens. Hungriest: B200, 365W, 0.62 kWh. At $0.15/kWh: $0.050 per 1M generated tokens.

Our verdict

Phi-4 14B: 177 tok/s peak, the fastest 14B we've measured, with a ~10GB floor for 12GB cards. A polished generalist that we nonetheless rank behind DeepSeek, Dolphin and the Mistral coders for the technical work most local users actually do. Choose it for speed and general answers, not as your coding brain.

FAQ

Is Phi-4 14B the fastest 14B?

In our data, yes: 177 tok/s peak vs 165 (Qwen3 14B) and 156 (DeepSeek-R1 14B) on the same harness. It's also efficient in class: 168 tok/s at 203W on the H200.

Then why isn't it your 14B pick?

Because mid-size local use skews heavily toward coding and technical work, where the DeepSeek distill's reasoning and the coder-tuned models (Codestral, Qwen-Coder) deliver more useful output. Phi-4's edge is polished general answering, a narrower niche.

What GPU does it need?

12GB and up. Measured peak was ~10GB at Q4_K_M. An Arc B580 or RTX 3060 12GB clears it; 16GB gives comfortable context room.

What's its best role in a multi-model setup?

The fast generalist seat: it answers directly at 14B quality while your reasoning models deliberate. In a consensus panel it provides the quick baseline answer the others get checked against.

Phi-4 14B or Phi-4 Mini?

Different jobs. Mini (3.8B, ~4GB) is our small-tier quality pick and runs anywhere. The 14B is a genuine step up in depth but enters the fiercest weight class we cover. Its competition is much stronger.