Llama 3.2 3B · 11 GPUs measured first-party · llama.cpp Q4_K_M · Updated July 2026

What GPU Do You Need for Llama 3.2 3B?

Llama 3.2 3B is the bigger sibling in Meta's small-model pair, a solid base at 436 tok/s peak (RTX PRO 6000 Blackwell) with a ~3GB measured floor. We ran it on 11 GPUs with the same pinned llama.cpp Q4_K_M harness as everything else on this site.

Benchmarked weights: bartowski/Llama-3.2-3B-Instruct-GGUF

Where it stands in the small tier. This is a good base model: clean behavior, the full weight of the Llama ecosystem, and enough capability for real summarization and structured-output work. But it sits in the most contested weight class we benchmark, and our honest verdict is that Phi-4 Mini (3.8B, 398 tok/s) answers better for essentially the same hardware budget. Where the 3B wins instead: it's ~10% faster, its ~3GB floor is a touch lighter, and if your deployment story involves fine-tuning, Llama's tooling has no equal.

The measurements. The chart top is flat again, 436, 432, 425 tok/s across three very different cards, because 3B parameters can't stress modern silicon. The practical rows are lower down: 107 tok/s on an L4 at 50W measured, 85 on a T4. Any GPU made in the last five years turns this model into an instant-response tool; the hardware decision is purely about watts and cost.

Our verdict

Llama 3.2 3B: 436 tok/s peak, ~3GB floor, a solid, fast base with unbeatable tooling. As a shipped assistant we'd take Phi-4 Mini's answer quality; as a foundation to tune, or a speed-first workhorse, the Llama is the right pick.

FAQ

Llama 3.2 3B or Phi-4 Mini?

Phi-4 Mini for out-of-the-box answer quality. That's our small-assistant pick. This 3B for fine-tuning (Llama tooling is the deepest there is) and for a ~10% speed edge (436 vs 398 tok/s peak).

What GPU does it need?

~3GB measured peak, any 4GB card fits it. On an L4 it does 107 tok/s at 50W; on anything modern it feels instantaneous.

Is it good enough for user-facing chat?

For focused tasks (summarize, extract, answer over provided context) yes. For open-ended assistant work, the 8B tier is where quality stops feeling budget. That's one step up in our lineup.

Why is the top of the chart so flat?

436 vs 432 vs 425 tok/s: a 3B model can't saturate big cards, so bandwidth monsters and workstation silicon converge. Buying premium hardware for small models buys almost nothing.

What's its best production role?

The tunable middle: big enough to learn your task from a fine-tune, small enough to deploy anywhere. If you're customizing a model to one job, this base plus Llama's recipe ecosystem is the lowest-friction path we know.