Llama 3.2 1B · 11 GPUs measured first-party · llama.cpp Q4_K_M · Updated July 2026

What GPU Do You Need for Llama 3.2 1B?

Llama 3.2 1B holds the single fastest LLM record in our entire database: 893 tokens per second on the RTX PRO 6000 Blackwell. Measured on 11 GPUs (llama.cpp, Q4_K_M) with a ~2GB floor, it's Meta's smallest model, and the benchmark ceiling for what 'fast' means in local inference.

Benchmarked weights: bartowski/Llama-3.2-1B-Instruct-GGUF

A good base, with one caveat. Llama 3.2 1B is exactly what it says: a clean, well-trained base model with the largest tooling ecosystem in open source behind it. As raw material for fine-tunes and as maximum-velocity pipeline glue, it's excellent. Our honest ranking for the tiny tier, though: if the job is being a small *assistant*, Phi-4 Mini (3.8B) answers noticeably better for a modest speed cost; if the job is running client-side on user devices, Qwen3 0.6B's sub-1B size keeps weak hardware viable. This model's lane is speed itself, batch work where 893 tok/s is the entire specification.

What the record run tells us. The top three cards are separated by barely 1%: 893, 889, 882 tok/s. At 1B parameters, nothing can differentiate big silicon, the model simply can't load it. Which makes the sensible deployment the opposite of glamorous: an L4 does 258 tok/s at 46W measured, a seven-year-old T4 does 201, and both are overkill for most pipelines. This is the model class where hardware stops mattering.

Our verdict

Llama 3.2 1B: 893 tok/s, the fastest LLM result we've ever measured, with a ~2GB floor that runs on anything. A good base model and the definitive speed play; for tiny-tier assistant quality, our pick shifts to Phi-4 Mini.

FAQ

What's the fastest GPU result for Llama 3.2 1B?

893 tok/s on the RTX PRO 6000 Blackwell, the fastest LLM measurement in our whole database. The B300 and B200 land within 1%: at 1B parameters, top-end hardware is indistinguishable.

What hardware does it actually need?

Almost none, ~2GB peak at Q4_K_M. A T4 from 2018 delivers 201 tok/s. For batch pipelines, the cheapest efficient card you can find (L4: 258 tok/s at 46W) is the right answer.

Llama 3.2 1B or Phi-4 Mini as a small assistant?

Phi-4 Mini, in our view, its 3.8B answers are clearly stronger, and it still measured 398 tok/s peak. Choose this 1B when velocity or fine-tuning is the goal, not conversation quality.

Can it run on user devices like Qwen3 0.6B?

It's borderline, at ~1.2B parameters it's past the sub-1B line where weak devices keep up. On decent laptops yes; for the broadest 'runs on anything the user owns' guarantee, the 0.6B remains the safer bet.

What is it best at?

Being a base. The Llama ecosystem, fine-tuning recipes, tooling, deployment paths, is the deepest in open source, and this is its lightest expression. Speculative decoding drafts, edge summarizers, and custom tunes are its natural jobs.