llamaperf

SmolLM2

1 report

As of 12 Oct 2026, SmolLM2 0.1B at 4-bit on the hardware it is most run on, with the median of plain runs (one device, one request, no speculative decoding, the whole model in its memory):

SmolLM2 VRAM requirements by size and quant →

How does SmolLM2 run on your hardware?

Pick your GPU or Mac, then read the speeds people reported for SmolLM2 on it, or estimate memory fit and speed.

Free to use. No account needed. Memory estimates and community measurements are labelled separately.

Run SmolLM2 yourself? Add your numbers.

Your coding agent can run a speed test on your machine and send it here with a free account. Or paste a result you've already posted.

Reported performance for SmolLM2

Filter this model’s reports by setup →
Thin page (1 of 3 reports needed for indexing). Add yours.
NVIDIA hardware
generation:
165.2 tokens/s
quant:
Q4_K_M (GGUF)

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is shown only when the source reports it; generation speed cannot tell us time to first token. Check the full setup before comparing.

User benchmarks SmolLM2-135M at 165.2 tok/s on a Jetson Orin Nano Super 8GB at 25W power mode. Setup is llama.cpp CUDA with Q4_K_M quant, 2048-token context, 256-token generation, single request, all layers on GPU. 25W beats 15W by 35-47% throughput and edges out MAXN on tokens per joule; llama.cpp is 1.37x faster than Ollama at 25W. The model uses 101 MB and draws about 5.6 W.

Oct 12, 2026

Get a weekly email of new SmolLM2 reports on any GPU.

Email me new reports

Reported configurations

Individual observations from this page, not expected speeds or a ranking. Select a model to open its report, with the source and offloading conditions. Reported context may be a limit rather than actual input length.

Model / hardwareQuant / engineReported contextGeneration
SmolLM2 0.14B 0.1B
NVIDIA Jetson Orin Nano Super 8GB
Q4_K_M
llama.cpp
2,048165.2 tokens/s