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 2.3TBDeepSeek model
DeepSeek V4 Hardware Requirements & Server Compatibility
The released repository contains about 159.6GB of weight files. It is a multi-GPU deployment for this product range: 192GB aggregate is the arithmetic floor, while 384GB or more is the safer server planning point for runtime and context headroom.
- Model version
deepseek-ai/DeepSeek-V4-Flash- Family and variant
- DeepSeek V4 Flash · DeepSeek-V4-Flash
- Source version
60d8d70770c6- Source updated
- 22 June 2026
DeepSeek DeepSeek V4 Flash
deepseek-ai/DeepSeek-V4-Flash - Minimum GPU memory
- At least 176GB aggregate GPU memory with a supported sharding route
- Recommended hardware
- 384GB aggregate or more for operating headroom
- Licence
- MIT License
- Useful for
- Language & reasoning, Coding & agents
Buyer verdict
Where DeepSeek V4 Flash is a sensible fit
DeepSeek V4 Flash is relevant for long-context reasoning, software work and agentic services that need more capability than a small workstation model but do not justify the 1.6T-parameter V4 Pro checkpoint.
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
- At least 176GB aggregate GPU memory with a supported sharding route
- Recommended
- 384GB aggregate or more for operating headroom
This floor adds only modest overhead to the repository files and assumes a bounded context.
This is a planning recommendation. Context, concurrency and runtime behaviour remain acceptance variables.
Technical specification
DeepSeek V4 Flash model and hardware facts
Specifications shown for source version
60d8d70770c6776ff598c94bb586a859a38244f1, updated
22 June 2026.
- Architecture
- MoE, 284B total / 13B active
- Context
- 1,000,000 tokens
- Precision
- Mixed FP4 + FP8
- Repository weights
- 159.62GB
- Modality
- Text → text
- Licence
- MIT
Product compatibility
DeepSeek V4 Flash 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 176GB 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 176GB 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 176GB 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 176GB 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
- One-million-token model context with an architecture designed to reduce long-context compute and cache cost.
- Three response modes: non-think, Think High and Think Max.
- MIT-licensed repository and weights.
- Lower active parameter count than the total MoE size suggests.
Where to be cautious
- The official repository's local instructions are specialist and require model conversion and current runtime support.
- The one-million-token ceiling is not a practical default; DeepSeek recommends at least 384K only for Think Max.
- PCIe aggregate memory is not an automatic pool. Sharding support and throughput depend on the runtime and topology.
- The model card and repository metadata expose different size views because the release uses mixed numerical formats.
Serving software
- DeepSeek reference inference
- Transformers
- Specialist serving engines after version check
Before installation
- Pin the exact release commit and use DeepSeek's inference directory rather than a generic launch command.
- Start below 128K context and increase only after measuring cache growth, time to first token and output latency.
- For PCIe systems, verify tensor and pipeline parallel support before treating aggregate VRAM as usable.
- Keep V4 Flash and V4 Pro records separate. Their 284B and 1.6T envelopes are not interchangeable.
System requirements
- GPU layout
- Plan for at least 2 GPUs and validate model parallelism on the final topology.
- Context and cache
- The model context ceiling is not a guaranteed serving target. KV cache, batch size and concurrent sessions need separate capacity tests.
- System RAM
- Size system memory for model loading, runtime overhead, preprocessing and any CPU offload used by the final configuration.
- Storage
- Allow space for the pinned checkpoint, runtime images, caches, logs and at least one rollback version.
- Serving software
- Validate the exact checkpoint with DeepSeek reference inference, Transformers, Specialist serving engines after version check before acceptance.
- Representative workload
- Benchmark representative prompts or media at the required context, quality, latency and concurrency.
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
MIT License
Commercial use: permitted
The repository and weights state MIT licensing. Application, data and output governance remain separate responsibilities.
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
DeepSeek V4 Flash deployment FAQ
Can DeepSeek V4 Flash run on one 96GB GPU?
Not as the named repository checkpoint. Its weight files total about 159.6GB, before runtime and cache allocations.
Will it run on Studio 192?
The physical 192GB total clears the file-size floor, but it is split across two GPUs. It may fit only when supported software can divide the model across both GPUs.
Is one million tokens a realistic production context?
It is a model ceiling. Production context should be sized from the actual prompt distribution, latency target and concurrency, then measured.
What a complete DeepSeek V4 Flash 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.
