01What it can do
- Massive native context up to ~1M tokens
- Sparse MoE: only ~3B active, with solid throughput for its size
- Permissive MIT license
- Focused on reasoning and chat
- —~40 GB VRAM in Q4: requires multiple GPUs or a lot of RAM
- —Recent model: limited track record and public benchmarks
- —No Ollama tag — install via HuggingFace
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 LongCat Flash Lite Sparse 69B-A3B?
To run LongCat Flash Lite Sparse 69B-A3B locally with Q4 quantization, you need about 40 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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Too large for your machine? The kit gives you the model that fits in your VRAM
- Lifetime online access
- PDF + files
- Lifetime updates
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.