Llama-3.2-1B-Instruct-FP8-dynamic vs SmolLM-1.7B-Instruct-quantized.w4a16
Specs, VRAM requirements and download trends — updated 15 August 2026.
RedHatAI · A · Open weights
Params
1.5 B
Context
131,072
30d
170 K
llama3.2 licence · conditional
nm-testing · B · Open weights
Params
1.8 B
Context
2,048
30d
152 K
apache-2.0 · commercial OK
Specification comparison
Differences are highlighted; identical values are muted.
| Specification | Llama-3.2-1B-Instruct-FP8-dynamic | SmolLM-1.7B-Instruct-quantized.w4a16 |
|---|---|---|
| Parameters | 1.5 B | 1.8 B |
| Architecture | LlamaForCausalLM | LlamaForCausalLM |
| Context length | 131,072 | 2,048 |
| Licence | llama3.2 | apache-2.0 |
| Commercial use | Conditional | Allowed |
| Languages | 8 | 1 |
| Downloads 30d | 170,223 | 151,608 |
| Downloads all time | 13.9 M | 5.8 M |
| Quantizations on the Hub | SAFETENSORS | SAFETENSORS |
| Gated | No | No |
| First seen on the Hub | 2024-09-25 | 2024-08-23 |
Download trend
Daily snapshots, last 90 days (13 Aug – 10 Nov)
1.3 M152 K
Llama-3.2-1B-Instruct-FP8-dynamic
SmolLM-1.7B-Instruct-quantized.w4a16
A overtook B on 24 Oct
VRAM side by side
On RTX 4090 · 24 GB · 8K context unless noted
SmolLM-1.7B-Instruct-quantized.w4a16 · fp16
2.7 GB / 24 GB
Llama-3.2-1B-Instruct-FP8-dynamic · fp16 @ 128K ctx
6.3 GB / 24 GB
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