Kimi K2.5 VRAM Requirements: What Fits on Common GPUs and How to Reduce Costs

Developers exploring Kimi K2.5 quickly encounter one core problem: its 1T-parameter MoE design and 256K context window push VRAM requirements far beyond consumer GPUs—especially when you need long context + concurrency.

This article explains what actually consumes VRAM (weights vs. KV cache), compares memory needs across FP16 / INT8 / INT4, and provides practical, low-cost deployment paths—including quantization, KV-cache compression, offloading strategies, cloud GPUs, and API usage.

Kimi K2.5 VRAM Requirements

Kimi K2.5 is released in multiple GGUF quantization variants, each with a very different memory footprint. In practice, VRAM requirements are primarily determined by the chosen quantization, while long context and concurrency further increase memory pressure via KV cache.

The table below summarizes commonly used GGUF quantization levels and their recommended GPU configurations, based on Unsloth’s reported memory requirements and Novita AI’s suggested instance setups.

Quantization	Memory Requirements	Recommended Configuration
Q8_0	1093 GB	8× NVIDIA H200 (1128 GB VRAM)
Q6_K	845 GB	8× NVIDIA H200 (1128 GB VRAM)
Q4_K_M	623 GB	8× NVIDIA A100 80GB (640 GB VRAM)
Q4_0	583 GB	8× NVIDIA A100 80GB (640 GB VRAM)
Q3_K_M	492 GB	8× NVIDIA A100 80GB (640 GB VRAM)
Q2_K	376 GB	8× NVIDIA A100 80GB (640 GB VRAM)

Recommended GPU Configurations by Quantization

These configurations provide minimal but practical headroom above the raw model footprint, allowing for runtime overhead and limited KV cache usage. Higher-bit quantizations (such as Q8_0 and Q6_K) typically require H200-class GPUs, while Q4–Q2 variants can be deployed cost-effectively on A100 80GB clusters.

In real deployments, increasing context length or concurrency can push KV cache memory to become the dominant VRAM consumer, even when using low-bit GGUF quantization.

Why Kimi K2.5 Requires Massive VRAM？

Model Overview:

Spec	Value
Architecture	Mixture of Experts (MoE)
Total parameters	1T
Experts	384 total, 8 active per token
Context length	256K
Attention mechanism	MLA (per model specs)

Kimi K2.5’s memory pressure comes from two separate multipliers: (1) weight storage/sharding for a 1T MoE, and (2) KV-cache growth at 256K context, which can dominate total VRAM once you scale concurrency.

Mixture of Experts (MoE)

• With MoE, you don’t “use all parameters every token,” but you still need to store and route expert weights efficiently, and in practice you need multi-GPU sharding (tensor/expert parallel).

256K Context = KV cache scales fast

• KV cache grows with sequence length and concurrency.
• If you run multiple long requests simultaneously, KV quickly becomes the limiting factor even when weights are INT4.

Quantized KV cache helps (but needs the right backend)

Both SGLang and vLLM support quantized KV cache (e.g., FP8) to reduce KV memory footprint—often close to ~2× savings for KV.

How to Run Kimi K2.5 Locally at the Lowest Cost?

Kimi K2.5 can run locally only with extreme quantization + heavy offloading. The cheapest approach is to shrink the model and push most of the weights to RAM or disk instead of VRAM.

• Unsloth provides a Dynamic ~1.8-bit (1–2 bit) GGUF for Kimi K2.5, shrinking the model’s storage footprint from ~600GB down to ~240GB.
• Unsloth’s practical rule: disk + RAM + VRAM ≥ 240GB (more offloading = slower).

Kimi K2.5 Deployment Guide on Novita AI

• Step1：Register an account: Visit https://novita.ai/ to create/ log in your Novita AI account. Navigate to the GPUs section to view available GPU offerings and start your deployment.

• Step2：Choose GPU servers & templates: Select a template (PyTorch / CUDA), then pick your GPU configuration.

• Step3：Customize Your Deployment: Customize your environment by selecting your preferred operating system and configuration options to ensure optimal performance for your specific AI workloads and development needs.

• Step4：Launch an instance Start the instance and deploy your serving stack. Your high-performance GPU environment will be ready within minutes, allowing you to immediately begin your machine learning, rendering, or computational projects.

How to Save Kimi K2.5’s Memory in Deployment?

• Use Low-Bit Weight Quantization First

For self-hosted deployments, low-bit quantization is mandatory. GGUF formats (such as Q4_K_M or Q2_K) and INT4 weight-only quantization significantly reduce the model’s memory footprint, making multi-GPU deployment feasible on A100- or H200-class clusters. This is the foundation of any cost-effective setup.

• Enable Quantized KV Cache for Long Context

Inference engines like vLLM and SGLang explicitly document that KV cache becomes the dominant GPU memory consumer at long context. Enabling FP8 or FP4 KV cache can substantially reduce memory usage, allowing more tokens or higher concurrency under the same VRAM budget. This optimization is especially important when pushing beyond 64K–128K context.

• Limit Concurrency for Long-Context Requests

KV cache memory grows with both context length and the number of concurrent sequences. A common production practice is to separate short-context and long-context workloads, capping concurrency for long-context requests to prevent KV cache from exhausting GPU memory.

• Use Offloading When VRAM Is the Bottleneck

For highly constrained environments, CPU or disk offloading can further reduce GPU VRAM usage by moving part of the model weights out of GPU memory. This approach trades throughput and latency for lower hardware requirements and is best suited for experimentation or non-latency-critical workloads.

• Treat Context Length as a Cost Control Knob

Even though Kimi K2.5 supports up to 256K context, setting a lower default context (for example, 8K–32K) dramatically reduces memory pressure. Long context should be enabled only for workloads that truly require it.

Another Effective Way to Use Kimi K2.5: Using API

If you don’t want to manage multi-GPU clusters, quantization, and KV-cache tuning, the simplest way to use Kimi K2.5 is via Novita AI’s Serverless API . You pay per token and can start immediately.

Parameter	Value
Model ID	moonshotai/kimi-k2.5
Context length	262,144 tokens
Max output	262,144 tokens
Input modalities	text, image, video
Output modality	text
Key features	Reasoning, Structured output, Function calling

Conclusion

Kimi K2.5’s deployment cost is mainly determined by quantization choice and KV-cache pressure at long context (up to 256K). If you want full control and predictable throughput, Novita AI GPU lets you run Kimi K2.5 on the right multi-GPU setup. If you want the fastest path to production without infrastructure overhead, Novita AI’s Serverless API provides 262K context with simple, pay-as-you-go pricing.

Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing affordable and reliable GPU cloud for building and scaling.

Recommended Reading

• Kimi K2.5 Now on Novita AI: Multimodal AI for Vision, Code and Agent
• Kimi K2.5 vs GLM-4.7: Which Agentic LLM Is Better?
• Connect Kimi K2.5 to OpenCode with Novita AI: A Agentic Coding Guide

Frequently Asked Questions