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Moonshot AI released Kimi K2.5 in January 2026, a trillion-parameter Mixture-of-Experts model that ships with native INT4 quantization. The model compresses from roughly 2TB at full precision to approximately 600GB in its released form. For users running a Mac Studio M3 Ultra with 512GB unified memory, deploying this flagship open-source model locally is achievable with the right quantization choices and inference optimizations.
The Mac Studio M3 Ultra ships with 512GB of unified memory, which theoretically accommodates Unsloth's 1.8-bit dynamic quantization (UD-TQ1_0), requiring approximately 240GB of disk space. According to Unsloth's official documentation, the baseline requirement for running Kimi K2.5 is "disk space + RAM + VRAM ≥ 240GB." Having 512GB of unified memory means the entire model can reside in memory without disk swapping, which is critical for achieving reasonable inference speeds.
Jeff Geerling's December 2025 benchmarks showed that a four-Mac Studio M3 Ultra cluster (1.5TB total memory) running Kimi K2 Thinking achieved approximately 28 tokens per second with total power consumption under 500 watts. This provides a reference point for single-machine configurations: expect inference speeds in the 5-15 tokens/second range on a single 512GB unit, depending on quantization precision and context length.
Kimi K2.5 uses a modified DeepSeek V3 architecture and ships natively in INT4 format. When selecting quantization for a 512GB Mac Studio, consider these three factors:
| Quantization | File Size | Memory Requirement | Use Case |
| UD-TQ1_0 (1.8-bit) | 240GB | 240GB+ | Memory-constrained setups accepting lower precision |
| UD-Q2_K_XL (2-bit) | 375GB | 380GB+ | Recommended balance between size and quality |
| UD-Q4_K_XL (4-bit) | ~600GB | 600GB+ | Near-native INT4 quality, requires 512GB+ |
For 512GB configurations, UD-Q2_K_XL strikes the optimal balance between quality and practicality. If quality trade-offs are acceptable, UD-TQ1_0 frees more memory for the context window. Since Kimi K2.5 ships natively at INT4, using Q4_K_XL or Q5 quantization essentially delivers near-full precision performance.
Deploying Kimi K2.5 on Mac Studio requires llama.cpp, the standard framework for running large language models on Apple Silicon. The complete installation process follows:
When compiling llama.cpp, disable CUDA and enable Metal acceleration for Mac environments:
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build \
-DBUILD_SHARED_LIBS=OFF -DGGML_METAL=ON
cmake --build llama.cpp/build --config Release -j --clean-first \
--target llama-cli llama-server
cp llama.cpp/build/bin/llama-* llama.cpp
Download the model using Hugging Face CLI with the specified quantization:
pip install -U huggingface_hub hf_transfer
hf download unsloth/Kimi-K2.5-GGUF \
--local-dir ~/models/kimi-k2.5 \
--include "*UD-Q2_K_XL*"
Launch inference with MoE-optimized configuration:
LLAMA_SET_ROWS=1 ./llama.cpp/llama-cli \
--model ~/models/kimi-k2.5/Kimi-K2.5-UD-Q2_K_XL-00001-of-00008.gguf \
--temp 0.6 \
--min-p 0.01 \
--top-p 0.95 \
--ctx-size 16384 \
--fit on \
--jinja
The --fit on parameter enables automatic GPU/CPU resource allocation. The LLAMA_SET_ROWS=1 environment variable provides modest performance improvements.
Kimi K2.5's MoE architecture activates only a subset of expert networks per inference pass. For memory-constrained environments, the -ot parameter offloads MoE layers to CPU while preserving GPU memory for attention layers and shared experts:
# Offload all MoE layers to CPU
-ot ".ffn_.*_exps.=CPU"
# Offload only up and down projection layers
-ot ".ffn_(up|down)_exps.=CPU"
# Offload only up projection layers (more GPU resources)
-ot ".ffn_(up)_exps.=CPU"
Offloading degree inversely correlates with inference speed: more offloading reduces GPU pressure but slows generation. With 512GB configuration using Q2_K_XL quantization and moderate MoE offloading, you can maintain usable speeds while reserving sufficient memory headroom.
Kimi K2.5 supports context lengths up to 256K tokens, but actual memory consumption scales linearly with context size. Unsloth documentation recommends starting with smaller context sizes to verify system stability:
| Context Length | Estimated Additional Memory |
| 16,384 tokens | Baseline |
| 32,768 tokens | +8-12GB |
| 65,536 tokens | +20-30GB |
| 98,304 tokens (recommended) | +35-50GB |
For a 512GB configuration with Q2_K_XL quantization (~380GB), approximately 130GB remains for KV cache and system overhead. In practice, setting context between 32K-64K tokens is a safer choice.
To integrate Kimi K2.5 into existing workflows or applications, launch an OpenAI-compatible API server:
LLAMA_SET_ROWS=1 ./llama.cpp/llama-server \
--model ~/models/kimi-k2.5/Kimi-K2.5-UD-Q2_K_XL-00001-of-00008.gguf \
--alias "kimi-k2.5" \
--min-p 0.01 \
--ctx-size 16384 \
--port 8001 \
--fit on \
--jinja \
--kv-unified
The --kv-unified parameter improves inference performance in llama.cpp. Once running, call the server via Python's OpenAI package:
from openai import OpenAI
client = OpenAI(
base_url="http://127.0.0.1:8001/v1",
api_key="sk-no-key-required"
)
response = client.chat.completions.create(
model="kimi-k2.5",
messages=[{"role": "user", "content": "Explain MoE architecture advantages"}]
)
print(response.choices[0].message.content)
Based on community reports and official data, performance expectations for a single Mac Studio M3 Ultra 512GB running Kimi K2.5:
| Configuration | Inference Speed | Notes |
| UD-TQ1_0 + full MoE offload | 5-10 tokens/sec | Most memory-efficient |
| UD-Q2_K_XL + partial offload | 8-15 tokens/sec | Balanced configuration |
| UD-Q4_K_XL + minimal offload | 10-21 tokens/sec | Near-full quality, careful memory management |
DEV Community testing indicates that dual Mac Studio M3 Ultra (512GB each) configurations achieve approximately 21 tokens/second. This suggests single-machine setups may approach half to two-thirds of this figure under optimal conditions.
Current limitations include incomplete vision support in llama.cpp for Kimi K2.5, so multimodal applications require future updates. Additionally, monitoring system temperature and memory pressure during extended high-load operation is advisable.
Moonshot AI's official API pricing runs USD 0.60 per million input tokens and USD 3.00 per million output tokens. For sustained research or development workloads, local deployment may offer better amortized costs:
| Option | Initial Investment | Monthly Operating Cost | Best For |
| Mac Studio M3 Ultra 512GB | ~USD 10,000 | Power ~USD 30-50 | High-frequency use, privacy-sensitive, offline requirements |
| Official API | None | Usage-based | Occasional use, need latest versions |
For workloads exceeding tens of billions of tokens monthly, local deployment provides clear marginal cost advantages. Conversely, light users benefit more from API pricing.
Running large language models locally involves numerous technical considerations. The following resources provide additional context:
DeepSeek: The AI Revolution Driven by Open Source Models and Distillation
Running DeepSeek R1: Mac Studio M3 Ultra, DGX, RTX 5090, A6000 Ada Deep Dive
Best LLM API Comparison: OpenAI, Llama, Gemini, Sonar, Claude
Sources
Unsloth Documentation. "Kimi K2.5: How to Run Locally Guide." https://unsloth.ai/docs/models/kimi-k2.5
Jeff Geerling. "1.5 TB of VRAM on Mac Studio - RDMA over Thunderbolt 5." December 2025. https://www.jeffgeerling.com/blog/2025/15-tb-vram-on-mac-studio-rdma-over-thunderbolt-5/
Hugging Face. "unsloth/Kimi-K2.5-GGUF." https://huggingface.co/unsloth/Kimi-K2.5-GGUF
Moonshot AI. "Kimi K2.5 Official Repository." https://huggingface.co/moonshotai/Kimi-K2.5
Author's Perspective
Kimi K2.5 represents a significant milestone in open-source AI, with its native INT4 quantization design demonstrating Moonshot AI's focus on deployment efficiency from the training phase onward. For developers and researchers who have invested in high-end Apple Silicon workstations, running trillion-parameter models locally has shifted from theoretical possibility to practical reality. That said, community feedback suggests extreme quantization (such as 1.8-bit) may degrade code generation quality, so quantization choices should align with actual application requirements.
— Ewan Mak, Tenten.co
Ready to evaluate local AI deployment architectures for your hardware, or looking to integrate large language models into your business processes? Schedule a consultation with the Tenten team to explore solutions tailored to your requirements.