Why Kimi K2 VRAM Requirements Are a Challenge for Everyone？

Kimi K2 is everywhere right now—people love how smart and versatile it is, especially with its standout agent abilities. All the new features have everyone talking, and let’s be real: a lot of us are curious if we can run Kimi K2 at home, and just how much VRAM you’d actually need to pull it off.

Exploring Kimi K2 VRAM Requirements

Kimi K2 is the newest model developed by Moonshot AI, renowned for its advanced agent abilities. Its capabilities are powered by the MuonClip Optimizer, which incorporates advanced instability resolution techniques. The agent is trained through simulated multi-turn tool-use scenarios spanning hundreds of domains and thousands of tools, with data filtered by LLM-based evaluators following task-specific rubrics. For reinforcement learning, Kimi K2 uses standard reward signals for verifiable tasks such as math and coding, while relying on rubric-based self-assessments for non-verifiable tasks like report writing. Continuous on-policy learning ensures ongoing improvement and enhanced judgment.

Detailed Hardware Requirements

As the largest open-source model, Kimi K2 features 1 trillion total parameters, with 32 billion activated at any given time. This immense scale requires substantial GPU resources to run locally. You can find more details in the following tables, sourced from Apx.

Full-Precision Models

Model Variant	Required VRAM (GB)	Minimum GPU Setup
Kimi K2-Base	2,401.52	H100/A100 80GB (x32)
Kimi K2-Instruct	2,401.52	H100/A100 80GB (x32)
Kimi-VL-A3B	51.87	A100/H100 80GB (x1)
Kimi-Dev-72B	177.27	A100/H100 80GB (x3)

Q4 Quantized Models (Reduced VRAM, Wider Accessibility)

Model Variant	Required VRAM (GB)	Minimum GPU Setup
Kimi K2-Base (Q4)	632.61	A100/H100 80GB (x8)
Kimi K2-Instruct (Q4)	632.61	A100/H100 80GB (x8)
Kimi-VL-A3B (Q4)	15.56	RTX 4080 (16GB) or RTX 3090/4090 (24GB)
Kimi-Dev-72B (Q4)	50	RTX 6000 Ada (48GB) (x2) or A100 80GB (x1)

Comparing VRAM Requirements with Other Models

Model Name	Precision / Context	VRAM Required	Minimum GPU Setup
DeepSeek R1 671B	FP16	1,421.82 GB	24 × H100 (80GB) 8 × H200 SXM (141GB)
DeepSeek V3 0324	FP16	1,425.02 GB	24 × H100 (80GB)
Llama 4 Maverick	FP16 / 128K context	938.1 GB	12 × H100 (80GB)

However, despite these improvements, overall deployment costs remain high due to the need for advanced hardware, ongoing electricity expenses, and specialized personnel for maintenance and optimization.

How to Select a GPU That Meets Kimi K2 VRAM Requirements

Attribute	Impacts
Architecture	Features, efficiency, compatibility
CUDA/Tensor/RT Cores	Model training/inference speed, graphics
VRAM/Memory BW	Model size supported, speed for big data
FP8/FP16/FP32/FP64	Precision, power, and speed for AI/science
Power (TDP)	Electricity, cooling, rack planning
NVLink/MIG/ECC	Scalability, reliability, multi-model use
Best For	Which workloads the GPU excels at
Cost/Deployment	Budget planning, ease of access

For a 1 trillion parameter model, focus on maximum VRAM, strong NVLink support, and efficient power usage per performance. This minimizes both cost and inference/training time.

Recommended GPUs for Running Kimi K2

Attribute	H100 (SXM)	B200
VRAM	80GB / 98GB HBM3	180 GB HBM3e
Memory Bandwidth	3.9 TB/s	8 TB/s per GPU
NVLink	Yes (NVLink 4.0/NVSwitch)	Yes (NVLink / NVSwitch 5th Gen)
FP8 Perf.	3.958 PFLOPS (dense)	9 PFLOPS
PCIe Support	SXM uses NVLink, not PCIe	NVLink only (NVL72)
Power (TDP)	700W (SXM)	1,000W
ECC	Yes	Yes
MIG	Yes	Yes

Recommended GPUs’ Price for Running Kimi K2

However, running Kimi K2 on your own hardware comes with a substantial financial burden. So, is there a more cost-effective way to leverage Kimi K2’s capabilities?

For Small Developers, Renting GPUs in the Cloud Can Be More Cost-Effective

In essence, cloud GPU solutions like Novita AI provide a cost-effective, flexible, and hassle-free way to access top-tier computing power—empowering you to innovate faster, reduce operational overhead, and stay ahead in the fast-moving world of AI.

The lowest Price-Novita AI

Provider	GPU Type	Price (USD/hr)
Novita AI	H100 SXM 80GB	$2.56
Lambda	H100 SXM 80GB	$3.29
RunPod	H100 SXM 80GB	$3.20

Technical Challenges for Home Servers

• High upfront hardware costs and ongoing maintenance
• Difficulty scaling resources for fluctuating workloads
• Time-consuming hardware setup and configuration
• Limited access to the latest GPU technology

How can Cloud GPU Solve the Problem

• Cost-Effectiveness and No Upfront Investment
  Purchasing high-performance GPUs for local use can require tens of thousands of dollars in initial spending, plus ongoing infrastructure costs for power, cooling, and physical space. With cloud GPU services, you avoid these large investments entirely. The pay-as-you-go pricing model means you only pay for the GPU hours you actually use.
• Scalability and On-Demand Access
  Local GPU setups are usually fixed in capacity and can’t easily accommodate spikes in demand or new project requirements. In contrast, cloud platforms allow you to scale your GPU resources instantly.
• No Hardware Setup or Maintenance
  Managing GPUs locally often means dealing with complex hardware installation, configuration, driver updates, and routine maintenance. Cloud GPU platforms handle all infrastructure management for you, including hardware reliability, cooling, power supply, and system compatibility.

How to Access Kimi K2 on Cloud GPU like Novita AI?

Step1：Register an account

If you’re new to Novita AI, begin by creating an account on our website. Once you’re registered, head to the “GPUs” tab to explore available resources and start your journey.

Step2：Exploring Templates and GPU Servers

Start by selecting a template that matches your project needs, such as PyTorch, TensorFlow, or CUDA. Choose the version that fits your requirements, like PyTorch 2.2.1 or CUDA 11.8.0. Then, select the A100 GPU server configuration, which offers powerful performance to handle demanding workloads with ample VRAM, RAM, and disk capacity.

Step3：Tailor Your Deployment

After selecting a template and GPU, customize your deployment settings by adjusting parameters like the operating system version (e.g., CUDA 11.8). You can also tweak other configurations to tailor the environment to your project’s specific requirements.

Step4：Launch an instance

Once you’ve finalized the template and deployment settings, click “Launch Instance” to set up your GPU instance. This will start the environment setup, enabling you to begin using the GPU resources for your AI tasks.

For Efficiency and Ease of Use, Choose the API!

Cloud GPU Benefit	Remaining Challenge	How API Solves It
Cost-Effectiveness & No Upfront Investment	Manual setup and resource management can still be time-consuming for users.	APIs automate resource provisioning and job submission, reducing human effort and mistakes.
Scalability and On-Demand Access	Scaling resources often requires manual intervention or advanced configuration.	APIs enable programmatic, instant scaling and integration with your existing workflows.
No Hardware Setup or Maintenance	Users may still need to configure environments or manage dependencies.	APIs offer pre-configured environments and easy deployment, eliminating most setup steps.

Deployment API Guide

Novita AI integrates the Anthropic API to use kimi k2 in Claude Code
surpassing many industry providers.
It also provides APIs with 131K context, 131K max output, 2.01s latency, 11.06 TPS throughput, and costs of $0.57/input and $2.30/output, delivering strong support for maximizing Kimi K2’s code agent potential.

Novita AI

Step 1: Log In and Access the Model Library

Log in to your account and click on the Model Library button.

Step 2: Choose Your Model

Browse through the available options and select the model that suits your needs.

Step 3: Start Your Free Trial

Begin your free trial to explore the capabilities of the selected model.

Step 4: Get Your API Key

To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.

Step 5: Install the API

Install API using the package manager specific to your programming language.

After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for python users.

from openai import OpenAI
  
client = OpenAI(
    base_url="https://api.novita.ai/v3/openai",
    api_key="session_1g0vYAKH0Oir6vI6y4PZIGyFLVvuJiJDx0jZiEeYivQFmDr15mi83mWi-_bdrs0C-Q2hk281SCn1f4oUB49loQ==",
)

model = "moonshotai/kimi-k2-instruct"
stream = True # or False
max_tokens = 65536
system_content = ""Be a helpful assistant""
temperature = 1
top_p = 1
min_p = 0
top_k = 50
presence_penalty = 0
frequency_penalty = 0
repetition_penalty = 1
response_format = { "type": "text" }

chat_completion_res = client.chat.completions.create(
    model=model,
    messages=[
        {
            "role": "system",
            "content": system_content,
        },
        {
            "role": "user",
            "content": "Hi there!",
        }
    ],
    stream=stream,
    max_tokens=max_tokens,
    temperature=temperature,
    top_p=top_p,
    presence_penalty=presence_penalty,
    frequency_penalty=frequency_penalty,
    response_format=response_format,
    extra_body={
      "top_k": top_k,
      "repetition_penalty": repetition_penalty,
      "min_p": min_p
    }
  )

if stream:
    for chunk in chat_completion_res:
        print(chunk.choices[0].delta.content or "", end="")
else:
    print(chat_completion_res.choices[0].message.content)
  
  

Bottom line: Kimi K2 is a game-changer, but running it locally is tough unless you have crazy hardware. Cloud GPU services like Novita AI make it way easier (and cheaper) to get started and see what all the hype’s about.

Frequently Asked Questions

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

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