text model · Llama 4 · Windows
Can I run Llama 4 Maverick on AMD Ryzen AI Halo (128GB)?
No. Llama 4 Maverick needs ~231.7 GB even at Q4_K_M, but AMD Ryzen AI Halo (128GB) only has ~96 GB usable.
Needs ~231.7 GB even at Q4_K_M, but only ~96 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 135.7 GB: Llama 4 Maverick needs roughly 231.7 GB at Q4_K_M and AMD Ryzen AI Halo (128GB) leaves only about 96 GB usable for a model. No single tracked device has enough memory; Llama 4 Maverick needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~231.7 GB
- Usable on device
- ~96 GB
- Device memory
- 128 GB
Which quant fits
- Parameters
- 400B (MoE, 17B active)
- Q4_K_M size
- 226.09 GB
- Q8_0 size
- 396.57 GB
- Context
- 1000k
- Ollama tag
- llama4:128x17b
- Memory
- 128 GB unified
- Usable for weights
- ~96 GB
- Power draw
- ~120 W
- Best runtime
- llama.cpp (Vulkan/ROCm) / LM Studio
What you can run instead
FAQ
Can AMD Ryzen AI Halo (128GB) run Llama 4 Maverick?
No. Llama 4 Maverick needs ~231.7 GB even at Q4_K_M, but AMD Ryzen AI Halo (128GB) only has ~96 GB usable.
How much memory does Llama 4 Maverick need?
AMD Ryzen AI Halo (128GB) does not have enough memory. At Q4_K_M the weights are ~226.09 GB; with KV cache and runtime overhead, budget ~231.7 GB at a 4k context. It is a Mixture-of-Experts model (400B total / 17B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Llama 4 Maverick 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.
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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.