SmolLM-1.7B-Instruct-quantized.w4a16 vs Qwen2.5-1.5B-Instruct-AWQ
Specs, VRAM requirements and download trends — updated 16 August 2026.
nm-testing · A · Open weights
Params
1.8 B
Context
2,048
30d
152 K
apache-2.0 · commercial OK
Qwen · B · Open weights
Params
1.8 B
Context
32,768
30d
195 K
apache-2.0 · commercial OK
Specification comparison
Differences are highlighted; identical values are muted.
| Specification | SmolLM-1.7B-Instruct-quantized.w4a16 | Qwen2.5-1.5B-Instruct-AWQ |
|---|---|---|
| Parameters | 1.8 B | 1.8 B |
| Architecture | LlamaForCausalLM | Qwen2ForCausalLM |
| Context length | 2,048 | 32,768 |
| Licence | apache-2.0 | apache-2.0 |
| Commercial use | Allowed | Allowed |
| Languages | 1 | 1 |
| Downloads 30d | 151,608 | 194,969 |
| Downloads all time | 5.8 M | 6.6 M |
| Quantizations on the Hub | SAFETENSORS | SAFETENSORS |
| Gated | No | No |
| First seen on the Hub | 2024-08-23 | 2024-09-17 |
Download trend
Daily snapshots, last 90 days (13 Aug – 10 Nov)
1.3 M152 K
SmolLM-1.7B-Instruct-quantized.w4a16
Qwen2.5-1.5B-Instruct-AWQ
VRAM side by side
On RTX 4090 · 24 GB · 8K context unless noted
Qwen2.5-1.5B-Instruct-AWQ · fp16
2.5 GB / 24 GB
SmolLM-1.7B-Instruct-quantized.w4a16 · fp16
2.7 GB / 24 GB
Adjacent comparisons
Qwen3-0.6B vs Qwen2.5-1.5B-Instruct-AWQ
Qwen3-0.6B vs SmolLM-1.7B-Instruct-quantized.w4a16
Llama-3.2-1B vs SmolLM-1.7B-Instruct-quantized.w4a16
PowerMoE-3b vs SmolLM-1.7B-Instruct-quantized.w4a16
SmolLM-1.7B-Instruct-quantized.w4a16 vs h2ovl-mississippi-2b
Llama-3.2-1B-Instruct-FP8-dynamic vs SmolLM-1.7B-Instruct-quantized.w4a16