Ollama Library · qwen2
Can I run deepscaler?
A fine-tuned version of Deepseek-R1-Distilled-Qwen-1.5B that surpasses the performance of OpenAI’s o1-preview with just 1.5B parameters on popular math evaluations.
Parameter counts and memory belong to each build below. A family can contain several sizes and tasks; fitting in memory is not a quality ranking.
Calculate memory for a build and contextdeepscaler system requirements
Memory needed at 4K context, counting model weights, KV cache, compute buffers and runtime overhead. Download sizes come from the official Ollama registry.
| Build | Quantization | Download | Memory needed |
|---|---|---|---|
| deepscaler:1.5b-preview-q4_K_M1.5B parameters (tag)128K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 1.0 GB | ~1.7 GB |
| deepscaler:1.5b-preview-q8_01.5B parameters (tag)128K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q8_0 | 1.8 GB | ~2.4 GB |
| deepscaler:1.5b1.5B parameters (tag)128K documented maximum contextBuild sourceCatalog checked 2026-10-13 | F16 | 3.3 GB | ~4.1 GB |
Showing the 3 smallest non-alias builds. Choose any build and change the context. A context above the build's documented maximum is a hypothetical calculation, not a supported setting.
How to run deepscaler locally
- 1.Install Ollama for Windows, macOS or Linux.
- 2.Download this exact build in a terminal:
ollama pull deepscaler:1.5b-preview-q4_K_M - 3.Check the source for the build's task. For text generation, run
ollama run deepscaler:1.5b-preview-q4_K_M. Base/text builds may need a prompt template; an instruct build is usually the appropriate starting point for a conversation.
Frequently asked questions about deepscaler
- How much VRAM do I need to run deepscaler?
- At 4K context, the smallest published build of deepscaler (Q4_K_M) needs roughly 1.7 GB of GPU memory once weights, KV cache, compute buffers and runtime overhead are counted. Bigger quantizations and longer context windows need more. It can also run partly or entirely on the CPU using system RAM, more slowly.
- Can I run deepscaler without a dedicated GPU?
- A supported CPU runtime can use system RAM. The smallest listed build needs an estimated 1.7 GB at 4K context, plus headroom for your operating system. Check the build's runtime requirements. We have not measured its speed on your CPU.
- How big is the deepscaler download?
- The smallest published build is 1.0 GB. There are 3 builds in total, the largest being 3.3 GB. Leave some extra free disk space beyond the download itself.
- How do I run deepscaler locally?
- Install Ollama, then download a specific build with "ollama pull deepscaler:1.5b-preview-q4_K_M". For a build that supports text generation, use ollama run with the same tag. Check the model source for its task and prompt format.
Will deepscaler run on your PC?
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Or start from your memory: 8 GB VRAM, 12 GB VRAM, 16 GB VRAM, 24 GB VRAM or a 16 GB Mac.
Model data from the official Ollama registry · verified 13/10/2026