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

Fastest we measured

NVIDIA RTX PRO 6000 Blackwell Workstation Edition

435.6 tok/s on Llama 3.2 3B, the ceiling. Measured on our bench. 96GB of VRAM, $8,565 at launch.

Pros

• 435.6 tok/s on Llama 3.2 3B
• 96GB, clears the Llama 3.2 3B floor
• Rentable by the hour rather than bought

Cons

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

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.

About Llama 3.2 3B. Llama 3.2 3B: from meta-llama, 3.2B parameters, on Hugging Face since September 2024, Llama 3.2 Community licence (gated: accept the terms first). 2,823,943 downloads in the last 30 days and 4 community quantizations.

How it compares. H100 80GB HBM3: Llama 3.2 3B 422.9 tok/s, Qwen2.5-3B 395.5 (3B), Qwen2.5-Coder-3B 394.0 (3B), SmolLM3 3B 398.0 (3B), Granite 4.1 3B 332.3 (3B). Llama 3.2 3B beats all 4 here.

Cost on a rented GPU. 1M generated tokens of Llama 3.2 3B: $0.44 on a T4 ($0.14/hr, 3.2 hours), $0.69 on a RTX PRO 6000 Blackwell Workstation Edition ($1.08/hr, 38 min, 1.6x the cost).

Speed tiers for Llama 3.2 3B. 30+ tok/s: 11 (RTX PRO 6000 Blackwell Workstation Edition, B300, H200). 30 tok/s is roughly where replies outpace reading.

Reading your prompt. Before Llama 3.2 3B writes anything it reads the input: 20710.1 tok/s on the RTX PRO 6000 Blackwell Workstation Edition (0.2s for a 4,000-token prompt), 2567.2 on the T4 (1.6s). Long documents and big code files feel this number more than the generation speed.

VRAM for Llama 3.2 3B. Measured peak 2.7GB, so 8GB is the smallest common card size; smallest card it ran on: T4 (16GB). With long context: Q4_K_M 3GB (tested), Q2_K 3GB, Q3_K_M 3GB, Q5_K_M 4GB, Q6_K 4GB.

Power on Llama 3.2 3B. Most efficient: RTX PRO 6000 Blackwell Workstation Edition, 167W, 0.11 kWh per 1M generated tokens. Hungriest: B200, 325W, 0.21 kWh. At $0.15/kWh: $0.016 per 1M generated tokens.

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.