Qwen3-Coder-Next: The VRAM and Infrastructure Handbook

Qwen3-Coder-Next pushes autonomous coding to a new level with its 80B-parameter Mixture-of-Experts architecture and ultra-long context capabilities. While its sparse activation design reduces active compute per token, deploying it in practice still demands serious GPU memory planning — especially for long-context agent workflows.

For developers on Novita AI, the challenge is no longer just compute, but VRAM orchestration. This guide breaks down the VRAM requirements, hardware selection, and optimization strategies needed to deploy Qwen3-Coder-Next effectively.

VRAM Requirements for Qwen3-Coder-Next

Deploying Qwen3-Coder-Next requires a strategic distinction between Static VRAM (model weights) and Dynamic VRAM (KV cache and activations). Despite its low active compute footprint, the full 80B weights must be resident in memory to prevent the latency “death spiral” of swapping experts from system RAM.

Recommended GPU Configurations by Quantization

The static memory footprint is primarily determined by the quantization level. For the 80B architecture of Qwen3-Coder-Next, the following configurations are recommended:

Quantization	Memory Requirements	Recommended GPU Configuration
BF16	159 GB	2 × NVIDIA A100 (80GB)
Q8_0	85 GB	4 × NVIDIA L4 (24GB) or 2 × RTX 5090 (32GB)
Q5_K	57 GB	1 × NVIDIA A100 (80GB)
Q4_K_M	49 GB	1 × NVIDIA A100 (80GB)
Q3_K_M	38 GB	1 × NVIDIA L40S (48GB)

While the model can theoretically run at 4-bit (Q4_K_M) within ~49 GB, you must account for the OS overhead and the KV cache. This makes the 80GB A100 or H100 the safest choice for production-grade stability.

Dynamic VRAM: The 256K Context Advantage

Unlike traditional transformers that scale quadratically, Qwen3-Coder-Next uses Gated DeltaNet for 75% of its layers, offering linear scaling for long-range dependencies. However, the remaining 25% still utilize standard Softmax attention, meaning a full 256,144 token context window can still consume significant VRAM if not managed via KV cache quantization.

Choosing the Right GPU for Qwen3-Coder-Next

Selecting a GPU isn’t just about capacity; it’s about memory bandwidth. MoE models are bandwidth-intensive because the router must fetch different experts for every token.

• NVIDIA H100 (80GB): The premier choice. With 3.3 TB/s bandwidth, it maximizes the throughput of the 512-expert pool, supporting high-speed agentic loops and FP8 precision.
• NVIDIA A100 (80GB): The most reliable all-rounder for Q4/Q5 quantization. It offers 2.0 TB/s bandwidth and sufficient VRAM to handle large context windows without crashing.
• NVIDIA L40S (48GB): The budget-conscious professional’s choice. Ideally suited for Q3_K_M quantization, it provides a balance of high CUDA core counts and modern Ada Lovelace architecture for efficient inference.

How to Optimize VRAM Usage

To squeeze the full 256K context window into your available VRAM, you must leverage advanced inference techniques supported by frameworks like vLLM and SGLang.

• KV Cache Quantization: By quantizing the Key-Value cache to FP8, you can reduce its memory footprint by 50% without significant loss in recall accuracy.
• PagedAttention: This eliminates memory fragmentation by managing the KV cache in non-contiguous “pages,” allowing you to utilize up to 90%+ of your GPU’s VRAM for actual tokens.
• Automatic Prefix Caching (APC): Essential for coding agents. If your agent repeatedly analyzes the same codebase, APC reuses the KV cache from the code-prefix, slashing prefill latency and memory usage.
• Expert Offloading: Frameworks like llama.cpp allow you to offload specific MoE experts to system RAM. While this reduces speed, it enables running higher-precision models on GPUs with lower VRAM, such as the L40S.

Cloud GPUs: A Smart Choice for Small Developers

The hardware required for Qwen3-Coder-Next creates a significant barrier to entry, with dual-GPU workstations often exceeding $10,000 in capital expenditure. Novita AI provides instant access to enterprise-grade infrastructure, allowing you to scale your hardware to match your quantization needs.

By leveraging Novita AI GPU Cloud, developers can deploy H100 or A100 clusters on a pay-as-you-go basis. Our Spot Instances offer up to 50% savings, with the H100 starting at just $0.73/hr. This enables individual developers and startups to run the Qwen3-Coder-Next 80B model with full 256K context at a fraction of the cost of local ownership.

Alternative Ways to Use Qwen3-Coder-Next: The Serverless API

For developers who need to integrate Qwen3-Coder-Next into IDEs like Cursor or Cline without managing infrastructure, the Novita AI Serverless API is the most efficient solution.

• Fixed Pricing: Pay only $0.20 per 1M input tokens and $1.50 per 1M output tokens.
• Massive Context: The API natively supports the 262,144 token context, allowing you to feed entire repositories into the model.
• Cache Read Support: Novita offers specialized pricing for repetitive prompts, reducing costs for agentic workflows where the context remains largely static.
• Plug-and-Play: Fully compatible with OpenAI and Anthropic-style API structures, ensuring a 5-minute migration for any existing tool.

How to Get API Key

• Step 1: Create or Login to Your Account: Visit https://novita.ai and sign up or log in.
• Step 2: Navigate to Key Management: After logging in, find “API Keys”.
• Step 3: Create a New Key: Click the “Add New Key” button.
• Step 4: Save Your Key Immediately: Copy and store the key as soon as it is generated; it is shown only once.

Conclusion

Whether you require the raw power of a dedicated H100 instance or the seamless scalability of a Serverless API, Novita AI provides the infrastructure necessary to turn Qwen3-Coder-Next into a production-ready coding powerhouse. As the industry moves toward autonomous, agentic development, the synergy between high-sparsity MoE models and scalable cloud infrastructure will be the ultimate competitive advantage.

Ready to deploy? Explore our Model Library or check the latest GPU Pricing to start your journey with Qwen3-Coder-Next today.

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

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