DeepSeek-R1 Distill 32B vs DeepSeek-R1 Distill 1.5B
Size, context window, license, approximate VRAM and the minimum local hardware each model needs — computed from our catalog and compatibility engine, not benchmarks.
| DeepSeek-R1 Distill 32B | DeepSeek-R1 Distill 1.5B | |
|---|---|---|
| Parameters | 32B | 1.5B |
| Context window | 128K tokens | 128K tokens |
| License | MIT | MIT |
| ~VRAM @ 4-bit (Q4_K_M) | ~20 GB | ~1.5 GB |
| ~VRAM @ 8-bit (Q8_0) | ~34 GB | ~2.5 GB |
| Minimum device | NVIDIA GeForce RTX 3090 | NVIDIA GeForce RTX 3060 12GB |
| Recommended device | Supermicro 8x H100 SuperServer | Supermicro 8x H100 SuperServer |
| Deployment | Hybrid | Local / on-prem |
| Capabilities | Reasoning, Long context | Reasoning, Long context |
Highlighted cells mark the lighter / longer / more permissive side per row, for local deployment. Informational rows have no winner.
Bottom line
DeepSeek-R1 Distill 1.5B (~1.5B) is lighter than DeepSeek-R1 Distill 32B (~32B), so it runs on more modest hardware, while DeepSeek-R1 Distill 32B trades a larger footprint for more capacity. At 4-bit, DeepSeek-R1 Distill 1.5B needs about 1.5GB versus ~20GB, a meaningful gap when choosing a GPU. Both target a 128K context window. Both ship under permissive licenses, easing commercial use. Minimum viable hardware differs: DeepSeek-R1 Distill 32B starts on a NVIDIA GeForce RTX 3090, DeepSeek-R1 Distill 1.5B on a NVIDIA GeForce RTX 3060 12GB. Figures are approximate working-set estimates, not benchmarks — verify the exact release before committing hardware.
Pick DeepSeek-R1 Distill 32B if you have the memory to spare and want the larger model, or you want step-by-step reasoning.
Pick DeepSeek-R1 Distill 1.5B if you want the lighter footprint and cheaper hardware, or you want step-by-step reasoning.
A 24GB+ card (RTX 3090/4090) at 4-bit. The best locally-runnable reasoning option for most teams.
Runs on almost any hardware, including CPUs and mini PCs. A reasoning model you can put on the edge.
Run the winner on hardware you control
Pick the model that fits your footprint, then turn the right machine into a private AI Business OS — no per-seat data leaving your premises.