01What it can do
- Effective 256k context
- Unique hybrid
- 9 languages
- —Custom Jamba license (not OSI)
- —Partial llama.cpp support
- —Superseded by Jamba 1.6/1.7
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for Jamba 1.5 Mini?
To run Jamba 1.5 Mini locally with Q4 quantization, you need about 30 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.
Why this choice? Our complete guide on GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) →
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Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
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03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.