SmolLM2 1.7B for on-device llms
SmolLM2 1.7B is ranked #7 on LLMDex's on-device llms ranking out of 7 models we track for this use case. Below, the specific reasons it slots where it does, and when you should reach for an alternative.
Updated
At a glance
- Rank
- #7 of 7
- Context
- 8.2K tokens
- Output / 1M
- Pricing not published
- Released
- Nov 2024
Why SmolLM2 1.7B fits this task
Three things about SmolLM2 1.7B that map directly onto what this task rewards: Truly tiny; Apache-2.0; Runs on phones. Beyond the task-specific fit, SmolLM2 1.7B also brings truly tiny and apache-2.0, both of which compound when the workload broadens.
The criteria this task rewards
LLMDex ranks best on-device llms on 5 criteria , these are the axes the ranking uses, in priority order:
- Performance under 8B parameters
- Quantization tolerance (int4, int8)
- Memory footprint after quantization
- Inference speed on Apple Silicon / mobile NPUs
- License permissiveness for commercial use
How SmolLM2 1.7B scores on each axis
Where SmolLM2 1.7B costs you: quality very limited. For most teams this is acceptable on this workload, the value of the strengths above outweighs the cost. For cost-bound workloads or teams with strict latency budgets, run an eval against the next two ranked models on real data before committing.
Strengths that pay off here
- Truly tiny
- Apache-2.0
- Runs on phones
Tracked weaknesses
- Quality very limited
- No hosted API
When to pick something else
If you can pay slightly more or accept slightly different tradeoffs, Ministral 8B from Mistral ranks one position higher and tends to win on the hardest cases. Mistral's 8B edge model, designed specifically for on-device and on-prem deployment.
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Other models for on-device llms
- Phi-4 for on-device llms
Microsoft's 14B model, exceptional quality-per-parameter via curated synthetic training data.
Read guide - Phi-3.5 Medium for on-device llms
14B Phi-3.5, predecessor to Phi-4 with strong benchmark efficiency for its size.
Read guide - Gemma 2 9B for on-device llms
Google's mid-2024 open-weight 9B, strong quality for its size, friendly license.
Read guide - Qwen2.5-7B for on-device llms
Small Qwen, practical default for laptop and edge inference.
Read guide - Llama 4 8B for on-device llms
Meta's small Llama 4, built for on-device and edge inference.
Read guide
SmolLM2 1.7B for other use cases
Direct comparisons
Frequently asked
Is SmolLM2 1.7B good for on-device llms?
SmolLM2 1.7B is ranked #7 on LLMDex's on-device llms list. HuggingFace's tiny model line, punches above its weight on a strict on-device budget.How much does SmolLM2 1.7B cost for on-device llms?
Other has not published per-token pricing for SmolLM2 1.7B at the time of writing.What's a cheaper alternative to SmolLM2 1.7B for on-device llms?
Look at the full Best On-Device LLMs ranking for cheaper picks at lower ranks.When should I NOT use SmolLM2 1.7B for on-device llms?
Tracked weakness: Quality very limited. If that constraint is binding for your workload, the next-ranked model on this task is the safer pick.
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