Best larger-system route
Frontier Native 2.3TB
The smallest system in the range that reaches this model's preferred working allowance.
Explore Frontier Native 2.3TBQwen model
Qwen3-Coder-Next Hardware Requirements & Server Compatibility
Qwen3-Coder-Next is a current Qwen release for coding agents, repository work and tool-driven software development. The pinned repository contains approximately 159.358GB of model weights. Our 160GB aggregate across at least 2 GPUs figure is a calculated memory screen, while 192GB aggregate across at least 2 GPUs is the safer starting allowance for deployment testing.
- Model version
Qwen/Qwen3-Coder-Next- Family and variant
- Qwen3 Coder · Next 80B-A3B
- Source version
a7fbcb5c0e12- Source updated
- 3 February 2026
Qwen Qwen3-Coder-Next
Qwen/Qwen3-Coder-Next - Minimum GPU memory
- 160GB aggregate across at least 2 GPUs
- Recommended hardware
- 192GB aggregate across at least 2 GPUs
- Licence
- Apache License 2.0
- Useful for
- Coding & agents, Language & reasoning
Buyer verdict
Where Qwen3-Coder-Next is a sensible fit
Shortlist Qwen3-Coder-Next when coding agents, repository work and tool-driven software development is the priority and the exact licence and runtime suit the organisation. Choose hardware from the recommended allowance, then measure quality and performance on representative work before purchase.
These figures apply to the named model version. Quantisation, fine-tuning, context length, image resolution, batch size and serving software can materially change the hardware needed.
Hardware requirements
- Minimum
- 160GB aggregate across at least 2 GPUs
- Recommended
- 192GB aggregate across at least 2 GPUs
This allowance is derived from the pinned artifact and leaves only limited runtime headroom.
This working allowance creates room for runtime allocations and representative workload testing; it is not a performance benchmark.
Technical specification
Qwen3-Coder-Next model and hardware facts
Specifications shown for source version
a7fbcb5c0e12d62a448eaa0e260346bf5dcc0feb, updated
3 February 2026.
- Architecture
- Qwen3Next sparse mixture of experts
- Parameters
- 80B total / 3B active
- Context
- 262,144 tokens Architecture ceiling; serving capacity requires testing
- Modalities
- Text → text
- Repository weights
- 159.358GB Pinned first-party repository file total
- Native format
- BF16 safetensors
Product compatibility
Qwen3-Coder-Next compatibility across all 11 GPU systems
- Systems that fit without model splitting
- 2
- Systems needing multi-GPU validation
- 5
A system is listed as fitting when its GPU memory meets the requirement shown above. Speed, usable context, batch size and concurrent users still need testing with the final model and software configuration.
sme workstation
Team 32
- Per GPU
- 32GB
- Total GPU memory
- 32GB
- GPU count
- 1
This system provides 32GB per GPU and 32GB in total. The model requires 160GB aggregate across at least 2 GPUs.
sme workstation
Company 64
- Per GPU
- 32GB
- Total GPU memory
- 64GB
- GPU count
- 2
This system provides 32GB per GPU and 64GB in total. The model requires 160GB aggregate across at least 2 GPUs.
sme workstation
Studio 96
- Per GPU
- 96GB
- Total GPU memory
- 96GB
- GPU count
- 1
This system provides 96GB per GPU and 96GB in total. The model requires 160GB aggregate across at least 2 GPUs.
sme workstation
Studio 192
- Per GPU
- 96GB
- Total GPU memory
- 192GB
- GPU count
- 2
Total installed memory is sufficient, but the model must be divided across GPUs. Confirm that the serving software supports this GPU layout and test the required context and speed.
pcie rack
Value Rack 128
- Per GPU
- 32GB
- Total GPU memory
- 128GB
- GPU count
- 4
This system provides 32GB per GPU and 128GB in total. The model requires 160GB aggregate across at least 2 GPUs.
pcie rack
Value Rack 256
- Per GPU
- 32GB
- Total GPU memory
- 256GB
- GPU count
- 8
Total installed memory is sufficient, but the model must be divided across GPUs. Confirm that the serving software supports this GPU layout and test the required context and speed.
pcie rack
Enterprise 384
- Per GPU
- 96GB
- Total GPU memory
- 384GB
- GPU count
- 4
Total installed memory is sufficient, but the model must be divided across GPUs. Confirm that the serving software supports this GPU layout and test the required context and speed.
pcie rack
Enterprise 768
- Per GPU
- 96GB
- Total GPU memory
- 768GB
- GPU count
- 8
Total installed memory is sufficient, but the model must be divided across GPUs. Confirm that the serving software supports this GPU layout and test the required context and speed.
pcie rack
H200 1.1TB
- Per GPU
- 141GB
- Total GPU memory
- 1,128GB
- GPU count
- 8
Total installed memory is sufficient, but the model must be divided across GPUs. Confirm that the serving software supports this GPU layout and test the required context and speed.
frontier partner
Frontier Native 2.3TB
- Per GPU
- 288GB
- Total GPU memory
- 2,304GB
- GPU count
- 8
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
frontier partner
Frontier Rack 20TB
- Per GPU
- 288GB
- Total GPU memory
- 20,736GB
- GPU count
- 72
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
Deployment reality
Strengths, limits and runtime route
What it is good at
- Purpose-built for coding-agent workflows.
- Only 3B parameters are active per token.
- Native 256K context for large codebases.
- Official integration examples cover IDE and agent scaffolds.
Where to be cautious
- The model card recommends reducing context when out-of-memory errors occur.
- BF16 weights require multi-GPU loading in this product range below frontier systems.
- Repository weight size does not include every runtime allocation, KV cache, media encoder, batch or concurrent session.
- No GPU Servers benchmark result is claimed until this exact revision has been run under disclosed test conditions.
Serving software
- Transformers
- vLLM
- SGLang
Before installation
- Use source version a7fbcb5c0e12d62a448eaa0e260346bf5dcc0feb rather than an unversioned latest branch.
- Start with Transformers only after checking support for the exact architecture and numerical format.
- Use 192GB aggregate across at least 2 GPUs as the procurement starting point; the lower figure is a minimum-memory screen, not a service-level promise.
- Record precision, context, batch, concurrency, container digest, driver and measured latency in the acceptance report.
System requirements
- GPU layout
- The 160GB aggregate across at least 2 GPUs screen assumes supported tensor or pipeline parallel loading. Aggregate memory is not automatically pooled.
- Context and cache
- 262,144 tokens. Context length, KV cache, batch and concurrent sessions must be tested together; the architecture limit is not a throughput guarantee.
- System RAM
- Plan system RAM above the 159.358GB repository-weight footprint where model staging or CPU offload is required.
- Storage
- Reserve at least 399GB for the pinned weights, runtime cache and one rollback copy; production datasets and logs are additional.
- Serving software
- First-party material names Transformers, vLLM, SGLang. Pin the serving version and container digest because support for recent architectures can change quickly.
- Representative workload
- Acceptance-test coding agents, repository work and tool-driven software development at the intended quality, context, batch, concurrency and response-time target.
Performance: speed, usable context and concurrency depend on the selected system, software and workload. No benchmark is quoted on this page.
Commercial and legal boundary
Apache License 2.0
Commercial use: permitted
The official checkpoint is Apache 2.0 licensed.
Always retain the applicable notices and recheck the live terms for the intended organisation, territory, use and distribution route. Obtain legal advice where required; the official licence governs use.
Read the official licenceTechnical questions
Qwen3-Coder-Next deployment FAQ
What GPU memory does Qwen3-Coder-Next need?
Use 160GB aggregate across at least 2 GPUs as the lower memory screen and 192GB aggregate across at least 2 GPUs as the safer starting allowance. The final requirement changes with runtime, context, batch, concurrency and precision.
Which GPU Servers product should I start with for Qwen3-Coder-Next?
Use the compatibility table to find systems that meet the recommended allowance. A system that only meets the minimum can load the model in principle but may not meet the required context or response time.
Can Qwen3-Coder-Next be used commercially?
The official checkpoint is Apache 2.0 licensed. The linked official licence is authoritative; legal advice may be appropriate for the intended use and distribution route.
What a complete Qwen3-Coder-Next deployment needs
GPU memory is only one part of the system. Storage, data access, serving software, monitoring and administrator handover also affect a reliable deployment.
