Why pick Phi-4 Mini over Llama 3.2 3B?

Answer quality. The Phi training recipe (curated synthetic data) yields unusually strong reasoning for 3.8B, in our use it's the clear conversationalist of the small tier. Llama 3.2 wins on fine-tuning ecosystem and a ~10% speed edge instead.

What GPU does it need?

~4GB measured peak at Q4_K_M, 6GB cards run it comfortably, including laptop GPUs. Peak speed was 398 tok/s (B300), but even a T4 manages 66 tok/s.

Is it good enough as a primary assistant?

For everyday queries, drafting, explaining, quick answers, genuinely yes. Route hard reasoning and long-context work to a 14B+ model and this handles the volume tier admirably.

Phi-4 Mini or Qwen3 4B?

Same weight class, both excellent. Our split: Phi for answer polish, Qwen3 4B for multilingual breadth and the broader Qwen ecosystem. Both need ~4GB; try each against your actual prompts.

What about Phi-4 14B?

Bigger brother, 177 tok/s peak, ~10GB floor. Solid, but at 14B it competes with DeepSeek's distill and the 24B coding models, where our preference usually goes to those. Mini competes at 4B, where it's the standout.

Phi-4 Mini 3.8B · 11 GPUs measured first-party · llama.cpp Q4_K_M · Updated July 2026

What GPU Do You Need for Phi-4 Mini 3.8B?

Phi-4 Mini is Microsoft's 3.8B distillation of the Phi-4 recipe, and our pick for the best small assistant in the database. Measured on 11 GPUs (llama.cpp, Q4_K_M): 398 tok/s on the B300, ~4GB peak VRAM, with the RTX PRO 6000 delivering the best efficiency at 2.27 tok/W.

Benchmarked weights: bartowski/microsoft_Phi-4-mini-instruct-GGUF

398.16tok/s

Fastest: NVIDIA B300

measured, 3-run llama-bench

~4GB

VRAM needed (measured peak)

GPU-independent, applies to every card

GPUs measured

same pinned harness

2.27tok/W

Most efficient: NVIDIA RTX PRO 6000 Blackwell Workstation Edition

real power sampling, not TDP

What GPU Do You Need for Phi-4 Mini 3.8B?, tok/s, fastest 11

NVIDIA B300

398.16 tok/s

NVIDIA H200

392.38 tok/s

NVIDIA RTX PRO 6000 Blackwell Workstation Edition

391.04 tok/s

NVIDIA H100 80GB HBM3

388.86 tok/s

NVIDIA B200

375.74 tok/s

NVIDIA A100 80GB SXM4

239.17 tok/s

NVIDIA A100 40GB SXM4

238.48 tok/s

NVIDIA L40S

229.91 tok/s

NVIDIA A10G

157.5 tok/s

NVIDIA L4

87.04 tok/s

NVIDIA T4

66.43 tok/s

Measured on our own bench. A card absent from this chart has not been run on this model yet, or cannot fit it.

Phi-4 Mini 3.8B. Measured generation speed by GPU

NVIDIA B300398.16

NVIDIA H200392.38

NVIDIA RTX PRO 6000 Blackwell Workstation Edition391.04

NVIDIA H100 80GB HBM3388.86

NVIDIA B200375.74

NVIDIA A100 80GB SXM4239.17

NVIDIA A100 40GB SXM4238.48

NVIDIA L40S229.91

NVIDIA A10G157.5

NVIDIA L487.04

NVIDIA T466.43

GPU	tok/s	Prompt t/s	tok/W	Avg power
NVIDIA B300	398.16	10261.4	1.32	300.8 W
NVIDIA H200	392.38	15554.7	2.04	192.8 W
NVIDIA RTX PRO 6000 Blackwell Workstation Edition	391.04	20459.3	2.27	172.2 W
NVIDIA H100 80GB HBM3	388.86	16246.1	1.85	210.2 W
NVIDIA B200	375.74	16160.3	1.14	328.8 W
NVIDIA A100 80GB SXM4	239.17	7803.5	1.9	126.0 W
NVIDIA A100 40GB SXM4	238.48	7449.3	1.84	129.6 W
NVIDIA L40S	229.91	17917.7	1.5	153.0 W
NVIDIA A10G	157.5	7137.5	1.31	120.2 W
NVIDIA L4	87.04	5615.6	1.59	54.9 W
NVIDIA T4	66.43	2420.1	1.21	55.0 W

The small-tier quality pick. In the under-4B class, this is the model we'd actually hand to users. Microsoft's synthetic-textbook training gives it answer quality that punches above its parameter count, in our experience clearly ahead of Llama 3.2's small pair for assistant work, which is why our tiny-tier advice reads: Qwen3 0.6B for client-side deployment, Phi-4 Mini for the best small brain. At 398 tok/s peak it gives up only ~10% speed to the Llama 3B while being the noticeably better conversationalist.

Deployment math. The ~4GB floor is the sweet spot for modest hardware: 6GB laptop GPUs and every desktop card from the last half-decade clear it with room. The chart's flat top (398/392/391 across three card classes) says the usual: small models can't use big silicon, so deploy on efficiency: the PRO 6000's 391 tok/s at 172W is elegant, but an L4 at 87 tok/s and 55W is the volume-serving bargain.

Our verdict

Phi-4 Mini: 398 tok/s peak, ~4GB floor, and the best answers-per-parameter in our small-model lineup, our quality pick for the tiny tier. Fits nearly anything, including modest laptop GPUs; pair it with a big-model escalation path and most everyday queries never need the big model.

FAQ

Why pick Phi-4 Mini over Llama 3.2 3B?: Answer quality. The Phi training recipe (curated synthetic data) yields unusually strong reasoning for 3.8B, in our use it's the clear conversationalist of the small tier. Llama 3.2 wins on fine-tuning ecosystem and a ~10% speed edge instead.
What GPU does it need?: ~4GB measured peak at Q4_K_M, 6GB cards run it comfortably, including laptop GPUs. Peak speed was 398 tok/s (B300), but even a T4 manages 66 tok/s.
Is it good enough as a primary assistant?: For everyday queries, drafting, explaining, quick answers, genuinely yes. Route hard reasoning and long-context work to a 14B+ model and this handles the volume tier admirably.
Phi-4 Mini or Qwen3 4B?: Same weight class, both excellent. Our split: Phi for answer polish, Qwen3 4B for multilingual breadth and the broader Qwen ecosystem. Both need ~4GB; try each against your actual prompts.
What about Phi-4 14B?: Bigger brother, 177 tok/s peak, ~10GB floor. Solid, but at 14B it competes with DeepSeek's distill and the 24B coding models, where our preference usually goes to those. Mini competes at 4B, where it's the standout.