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Frontier Native 2.3TB
The smallest system in the range that reaches this model's preferred working allowance.
Explore Frontier Native 2.3TBMiniMax model
MiniMax M2.5 Hardware Requirements & Server Compatibility
MiniMax M2.5 is a current MiniMax release for coding, tool-using agents, search and office workflows. The pinned repository contains approximately 230.134GB of model weights. Our 256GB aggregate across at least 2 GPUs figure is a calculated memory screen, while 384GB aggregate across at least 4 GPUs is the safer starting allowance for deployment testing.
- Model version
MiniMaxAI/MiniMax-M2.5- Family and variant
- MiniMax M2 · 2.5
- Source version
f710177d938e- Source updated
- 10 March 2026
MiniMax MiniMax M2.5
MiniMaxAI/MiniMax-M2.5 - Minimum GPU memory
- 256GB aggregate across at least 2 GPUs
- Recommended hardware
- 384GB aggregate across at least 4 GPUs
- Licence
- Modified MIT License
- Useful for
- Language & reasoning, Coding & agents
Buyer verdict
Where MiniMax M2.5 is a sensible fit
Shortlist MiniMax M2.5 when coding, tool-using agents, search and office workflows 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
- 256GB aggregate across at least 2 GPUs
- Recommended
- 384GB aggregate across at least 4 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
MiniMax M2.5 model and hardware facts
Specifications shown for source version
f710177d938eff80b684d42c5aa84b382612f21f, updated
10 March 2026.
- Architecture
- Sparse MiniMax M2 mixture of experts
- Parameters
- 230.1GB released weight artifact
- Context
- 196,608 tokens Architecture ceiling; serving capacity requires testing
- Modalities
- Text → text
- Repository weights
- 230.134GB Pinned first-party repository file total
- Native format
- BF16 safetensors
Product compatibility
MiniMax M2.5 compatibility across all 11 GPU systems
- Systems that fit without model splitting
- 2
- Systems needing multi-GPU validation
- 4
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 256GB 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 256GB 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 256GB aggregate across at least 2 GPUs.
sme workstation
Studio 192
- Per GPU
- 96GB
- Total GPU memory
- 192GB
- GPU count
- 2
This system provides 96GB per GPU and 192GB in total. The model requires 256GB aggregate across at least 2 GPUs.
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 256GB 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
- Training emphasis on real coding and agent environments.
- Broad programming-language coverage.
- Long context.
- Official vLLM and SGLang deployment routes.
Where to be cautious
- The modified MIT terms differ from the standard MIT licence.
- The full checkpoint needs supported multi-GPU loading in this range.
- 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
- vLLM
- SGLang
- Transformers
Before installation
- Use source version f710177d938eff80b684d42c5aa84b382612f21f rather than an unversioned latest branch.
- Start with vLLM only after checking support for the exact architecture and numerical format.
- Use 384GB aggregate across at least 4 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 256GB aggregate across at least 2 GPUs screen assumes supported tensor or pipeline parallel loading. Aggregate memory is not automatically pooled.
- Context and cache
- 196,608 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 230.134GB repository-weight footprint where model staging or CPU offload is required.
- Storage
- Reserve at least 576GB for the pinned weights, runtime cache and one rollback copy; production datasets and logs are additional.
- Serving software
- First-party material names vLLM, SGLang, Transformers. Pin the serving version and container digest because support for recent architectures can change quickly.
- Representative workload
- Acceptance-test coding, tool-using agents, search and office workflows 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
Modified MIT License
Commercial use: conditional
MiniMax uses a modified MIT licence with additional terms; it is not the standard MIT text.
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
MiniMax M2.5 deployment FAQ
What GPU memory does MiniMax M2.5 need?
Use 256GB aggregate across at least 2 GPUs as the lower memory screen and 384GB aggregate across at least 4 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 MiniMax M2.5?
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 MiniMax M2.5 be used commercially?
MiniMax uses a modified MIT licence with additional terms; it is not the standard MIT text. The linked official licence is authoritative; legal advice may be appropriate for the intended use and distribution route.
What a complete MiniMax M2.5 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.
