text model · DeepSeek-V2 · Windows
Can I run DeepSeek-V2-Lite on AMD Ryzen AI Halo (128GB)?
Yes. DeepSeek-V2-Lite runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~12.2 GB of ~96 GB usable).
Runs at Q4_K_M using ~12.2 GB of ~96 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. AMD Ryzen AI Halo (128GB) leaves ~83.8 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~12.2 GB
- Usable on device
- ~96 GB
- Device memory
- 128 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run deepseek-v2:16b llama-cli -hf mradermacher/DeepSeek-V2-Lite-GGUF:Q4_K_M lms get mradermacher/DeepSeek-V2-Lite-GGUF - Parameters
- 16B (MoE, 2.4B active)
- Q4_K_M size
- 10.4 GB
- Q8_0 size
- 16.8 GB
- Context
- 32k
- Ollama tag
- deepseek-v2:16b
- Memory
- 128 GB unified
- Usable for weights
- ~96 GB
- Power draw
- ~120 W
- Best runtime
- llama.cpp (Vulkan/ROCm) / LM Studio
You could also run
Run DeepSeek-V2-Lite on other hardware
FAQ
Can AMD Ryzen AI Halo (128GB) run DeepSeek-V2-Lite?
Yes. DeepSeek-V2-Lite runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~12.2 GB of ~96 GB usable).
How much memory does DeepSeek-V2-Lite need?
AMD Ryzen AI Halo (128GB) has room to spare. At Q4_K_M the weights are ~10.4 GB; with KV cache and runtime overhead, budget ~12.2 GB at a 4k context. It is a Mixture-of-Experts model (16B total / 2.4B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run DeepSeek-V2-Lite on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/deepseek-v2-lite/amd-ryzen-ai-halo-128gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.