SmolLM3 3B · 11 GPUs measured first-party · llama.cpp Q4_K_M · Updated October 2026

What GPU Do You Need for SmolLM3 3B?

SmolLM3 3B is Hugging Face's fully open small model: not just open weights, but published training data and recipe. It's also quietly one of the fastest models in our database: 411 tok/s on the B300, with a ~3GB floor that fits practically any GPU made this decade. Measured on 11 GPUs, llama.cpp Q4_K_M.

Benchmarked weights: bartowski/HuggingFaceTB_SmolLM3-3B-GGUF

Fastest we measured

NVIDIA B300

411.2 tok/s on SmolLM3 3B, the ceiling. Measured on our bench. 288GB of VRAM, $40,000 at launch.

Pros

• 411.2 tok/s on SmolLM3 3B
• 288GB, clears the SmolLM3 3B 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

406.4 tok/s on SmolLM3 3B, lowest launch price that still fits. Measured on our bench. 96GB of VRAM, $8,565 at launch.

Pros

• 406.4 tok/s on SmolLM3 3B
• 96GB, clears the SmolLM3 3B floor
• Rentable by the hour rather than bought

Cons

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

What 'fully open' buys you. Most 'open' models publish weights and stop there. SmolLM3 ships the whole pipeline, dataset composition, training code, recipe decisions, which makes it the small model you can audit, reproduce and legally reason about with confidence. For companies with provenance requirements, or anyone fine-tuning who wants to know what's actually in the base, that transparency is a feature no benchmark column captures.

And the benchmarks are real anyway. 411 tok/s peak puts it ahead of every 3-4B rival we've measured on the same silicon (Llama 3.2 3B: 436 is the one exception; Phi-4 Mini: 398; Gemma 3 4B: 301), and the H200's 3.07 tok/W is the best mid-tier efficiency in the small class. With a ~3GB floor, deployment cost rounds to zero: an L4 does 109 tok/s at 56W, a T4 does 86. It competes honestly in the small tier on speed while being the only member you can fully inspect.

About SmolLM3 3B. SmolLM3 3B: from HuggingFaceTB, 3.1B parameters, on Hugging Face since July 2025, Apache 2.0 licence. 635,611 downloads in the last 30 days.

How it compares. H100 80GB HBM3: SmolLM3 3B 398.0 tok/s, Qwen2.5-3B 395.5 (3B), Qwen2.5-Coder-3B 394.0 (3B), Llama 3.2 3B 422.9 (3B), phi-2 340.1 (3B). 1 of 4 beat SmolLM3 3B here.

Cost on a rented GPU. 1M generated tokens of SmolLM3 3B: $0.44 on a T4 ($0.14/hr, 3.2 hours), $4.69 on a B300 ($6.94/hr, 41 min, 10.7x the cost).

Speed tiers for SmolLM3 3B. 30+ tok/s: 11 (B300, RTX PRO 6000 Blackwell Workstation Edition, H200). 30 tok/s is roughly where replies outpace reading.

Reading your prompt. Before SmolLM3 3B writes anything it reads the input: 20206.1 tok/s on the RTX PRO 6000 Blackwell Workstation Edition (0.2s for a 4,000-token prompt), 2527.7 on the T4 (1.6s). Long documents and big code files feel this number more than the generation speed.

VRAM for SmolLM3 3B. Measured peak 2.5GB, 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 2GB, Q3_K_M 3GB, Q5_K_M 4GB, Q6_K 4GB.

Power on SmolLM3 3B. Most efficient: RTX PRO 6000 Blackwell Workstation Edition, 176W, 0.12 kWh per 1M generated tokens. Hungriest: B200, 334W, 0.23 kWh. At $0.15/kWh: $0.018 per 1M generated tokens.

Our verdict

SmolLM3 3B: 411 tok/s peak, ~3GB floor, top-tier small-model efficiency, and the only model in our lineup with fully published training data and recipe. If auditability matters to your deployment, it's the obvious small pick; even if it doesn't, the speed stands on its own.

FAQ

What makes SmolLM3 different from other small models?

Full transparency: Hugging Face published the training data, code and recipe, not just weights. It's the only model we benchmark whose complete provenance you can inspect, relevant for compliance-sensitive deployments and informed fine-tuning.

What GPU does it need?

~3GB measured peak at Q4_K_M, any 4GB card fits it. Speed ranges from 411 tok/s (B300) to 86 tok/s on a seven-year-old T4.

How does it compare to Llama 3.2 3B and Phi-4 Mini?

Speed: between them (411 vs Llama's 436 and Phi's 398 tok/s peaks). Quality: competitive for its size, with Phi-4 Mini still our answer-quality pick. Transparency: SmolLM3 alone. Choose by which of the three axes your project values.

What's its efficiency profile?

Excellent: 3.07 tok/W on the H200, the best mid-tier result in our small-model data, and 109 tok/s at 56W on an L4 for cheap sustained serving.

Is it a good fine-tuning base?

One of the best-informed choices available: knowing the training data means knowing what you're building on. The published recipe also makes continued pretraining far less of a guessing game than with closed-data bases.