Qwen3 Coder 480B A35B VRAM: How Much Memory Do You Need？

With the rapid rise of Qwen 3 Coder 480B A35B Instruct, many developers are eager to see what it takes to run this powerful model locally. This guide will help you understand the hardware (especially VRAM) and technical requirements for local deployment, and compare it with API and cloud GPU options.

What is Qwen 3 Coder 480B A35B Instruct？

Qwen 3 Coder 480B A35B Instruct is Alibaba’s third-generation Qwen model, optimized for code, with 480B total parameters (35B active at a time), and trained to follow user instructions.

What is the A35B Meaning?

• Qwen 3: The third generation of Alibaba’s Qwen large language models.
• Coder: Specialized for programming and code-related tasks.
• 480B: The model has a total of 480 billion parameters (“B” = billion).
• A35B: “Active” 35 billion parameters are used for each inference (typical in Mixture-of-Experts models).
• Instruct: Fine-tuned to follow human instructions or prompts more accurately.

Qwen 3 Coder 480B Architecture and Benchmark

Advantages of Instruction-Following

Through large-scale Mixture-of-Experts (MoE) architecture, extensive reinforcement learning (especially long-horizon multi-turn RL), and a high ratio of high-quality instruction data, Qwen 3 Coder 480B not only understands complex instructions, but can autonomously call tools and plan over multiple steps, achieving true agentic, step-by-step, and dynamically adaptive instruction following—far beyond the “static code generation” paradigm of typical coding models.

Qwen 3 Coder 480B A35B VRAM

Qwen 3 Coder Inference VRAM

Quantization	Size (GB)	Recommended Hardware
Unquantized (FP16)	960	Cloud-based or large-scale enterprise servers
Q4_K_M	290	High-end server with 320GB+ RAM, or Apple Mac Studio (M4) 512GB
unsloth Q4_K_XL	276	Similar to Q4_K_M, or multi-GPU setups: 12-13x RTX 3090/4090, 9-10x RTX 5090, or 3x Blackwell RTX Pro 6000
unsloth Q2_K_XL	180	Apple Mac M2 Ultra with 192GB Unified Memory
Q3_K_L	115	Desktop with 24GB VRAM GPU and 128GB+ system RAM

Qwen 3 Coder Finetune VRAM

Quantization Type	Model Size (GB)
FP32	9281.92
BF16	6706.92
FP8	5419.42

Minimun VRAM for Qwen 3 Coder

Memory-Saving Tips

• Selective GPU Offload:
  • Keep the router and self-attention layers on the GPU for speed, while streaming the larger expert feedforward (FFN) weights from system RAM using regex-based masking. This balances performance and memory usage.

• Dynamic 2-bit Quantization:
  • Unsloth Dynamic Q2-K-XL uses adaptive 2-bit quantization, which preserves about 98% of the original model’s accuracy, while reducing memory requirements by half.

• KV Cache Quantization:
  • Using options like --cache-type-k q4_1 --cache-type-v q4_1 reduces the size of the key-value cache by four times, with less than 1 perplexity point (pp) loss in model performance.

• Flash Attention & High-Throughput Mode:
  • Compile llama.cpp with -DGGML_CUDA_FA_ALL_QUANTS=ON to enable efficient Flash-Attention for all quantization types. Use llama-parallel to support multi-user inference with high throughput.

• Context Truncation:
  • For chatbot applications, limit the conversational history to 8,000–16,000 tokens. Each additional 32,000 tokens increases FP16 KV cache memory usage by approximately 6 GB.

• Batching:
  • Process multiple requests in a single forward pass. Solutions like vLLM and high-throughput modes in llama.cpp help serve many users efficiently by amortizing router overhead.

VRAM Usage Comparsion

Feature	Qwen3 Coder 480B A35B Instruct	DeepSeek V3 0324	Kimi K2
GPU Model	H100	H100	H100
GPUs Used	12 GPU	24 GPUs	32 GPUs
Total Price	$30000 per GPU direct from NVIDIA	$30000 per GPU direct from NVIDIA	$30000 per GPU direct from NVIDIA
Cloud GPU Price (Novita AI)	$30.72/hr	$61.44/hr	$81.92/hr

Another Effective Way: Using API

Novita AI provides Qwen3 Coder 480B A35B Instruct APIs with 262K context, 66K max output, 6.82s latency, 76.35 TPS throughput, and costs of $0.95/input and $5/output, delivering strong support for maximizing Qwen 3’s code agent potential.

Novita AI

Aspect	API	Local GPU	Cloud GPU
Setup	Instant	Complex	Moderate
Maintenance	None	High	Medium
Cost	Highest/unit	Lowest (at scale)	Medium
Scalability	Automatic	Hard	Easy
Privacy	Data goes out	Full local	Data goes out
Customization	Least	Most	High
Best for	Fast start, small/medium, no infra	Large, stable workloads, max privacy	Large/variable workloads, custom models

Step 1: 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.

pip install 'openai>=1.0.0'
from openai import OpenAI
  
client = OpenAI(
    base_url="https://api.novita.ai/v3/openai",
    api_key="",
)

model = "qwen/qwen3-coder-480b-a35b-instruct"
stream = True # or False
max_tokens = 131072
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)
  
  

Qwen 3 Coder 480B A35B Instruct sets a new benchmark for code-focused large language models, but also comes with significant hardware demands if you want to run it locally. For most users, direct API access or cloud GPU rentals are the fastest way to experience its capabilities, while large enterprises with advanced infrastructure can consider local deployment. Carefully weigh your needs, budget, and technical resources to choose the best way to harness the power of Qwen 3 Coder.

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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