text model · Kimi · Windows

Can I run Kimi K2.7 Code on AMD Ryzen AI Halo (128GB)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Kimi K2.7 Code needs ~592.1 GB even at Q4_K_M, but AMD Ryzen AI Halo (128GB) only has ~96 GB usable.

Needs ~592.1 GB Device usable ~96 GB

Needs ~592.1 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 496.1 GB: Kimi K2.7 Code needs roughly 592.1 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; Kimi K2.7 Code needs a multi-GPU or high-memory rig.

Q4_K_M needed: ~592.1 GB
Usable on device: ~96 GB
Device memory: 128 GB

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Which quant fits

Quant ladder vs ~96 GB usable

Q2_K

~427.2 GB

Q3_K_M

~497.2 GB

Q4_K_M

~592.1 GB

Q5_K_M

~720.9 GB

Q6_K

~828.4 GB

Q8_0

~1070.9 GB

FP16

~2008.4 GB

The line marks AMD Ryzen AI Halo (128GB)'s ~96 GB budget; rungs past it are too large.

How to run it

On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).

Model Kimi

Parameters: 1000B (MoE, 32B active)
Q4_K_M size: 583.71 GB
Context: 256k

Full Kimi K2.7 Code requirements →

Device Windows

Memory: 128 GB unified
Usable for weights: ~96 GB
Power draw: ~120 W
Best runtime: llama.cpp (Vulkan/ROCm) / LM Studio

Best models for AMD Ryzen AI Halo (128GB) →

What you can run instead

FAQ

Can AMD Ryzen AI Halo (128GB) run Kimi K2.7 Code?

No. Kimi K2.7 Code needs ~592.1 GB even at Q4_K_M, but AMD Ryzen AI Halo (128GB) only has ~96 GB usable.

How much memory does Kimi K2.7 Code need?

AMD Ryzen AI Halo (128GB) does not have enough memory. At Q4_K_M the weights are ~583.71 GB; with KV cache and runtime overhead, budget ~592.1 GB at a 4k context. It is a Mixture-of-Experts model (1000B total / 32B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Kimi K2.7 Code 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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Markdown

[![Kimi K2.7 Code on AMD Ryzen AI Halo (128GB)](https://localmodel.run/badge/kimi-k2.7-code/amd-ryzen-ai-halo-128gb.svg)](https://localmodel.run/can-i-run/kimi-k2.7-code/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.