Updated 2026-10-23
DeepInfer hardware requirements and deployment limits
Check your accelerator before reserving a machine. Even with supported hardware, you need to allow for the model's memory use, request length and parallel configuration. A machine that runs an offline example may still need more resources to serve users.
What hardware and software does DeepInfer support?
Compare your machine with the support list below. Another accelerator from the same manufacturer may use different kernels or communication software, even if it has enough memory.
| Requirement | Documented scope |
|---|---|
| Model | DeepSeek-V4.1-Flash; default checkpoint: DeepSeek-V4.1-Flash-1017 |
| Accelerators | NVIDIA SM100 GPUs or Huawei Ascend 950 NPUs |
| Environment | Linux, Python 3.12 and GCC 13.0 or later |
| Platform runtime | CUDA 13 for NVIDIA; CANN 9.2 for Ascend |
| Release scope | Offline inference; an online service needs additional infrastructure |
Sources
How should you estimate memory and device count?
Estimate disk space and device memory separately. The checkpoint's download size is not its peak memory use. In memory, allow for weights, runtime allocations, KV state at your chosen context length, concurrent requests and space for temporary build or execution work.
Choose how to split the work across devices. The examples cover expert, data and context parallelism, but their device counts will not suit every workload. Begin with short inputs and few requests, using enough devices to load the model and run your chosen configuration.
The accelerator counts in the published benchmarks describe those tests, not a minimum installation. Before booking hardware, write down the checkpoint, device connections, parallel settings and the result you need from the trial.
| Record before booking | Question to resolve |
|---|---|
| Checkpoint and precision | Which exact weights and runtime configuration will be loaded? |
| Context and concurrency | What is the longest request and how many are active together? |
| Parallel groups and interconnect | Do the devices and network support the intended layout? |
| Memory headroom | What remains after weights, cache and temporary allocations? |
| Trial goal | What output quality, latency and throughput do you need? |
Can you use Windows, a Mac or another GPU?
The README does not list these platforms. A virtual environment or compatibility layer alone cannot make the accelerator kernels work. If you find a community port, check its own hardware requirements and test results.
Our local-deployment guides cover Mac and GGUF experiments with other runtimes. Those instructions do not demonstrate that DeepInfer runs on a Mac.
What is still needed for an online API service?
An offline run is only part of an online service. DeepSeek lists KV cache infrastructure, separation of vision encoding, prefill and decode, and load balancing across data-parallel replicas as parts not fully shipped in this release.
Recipe translates request and response formats. You still have to connect inference, HTTP transport and tool execution, then handle routing, recovery, resource limits and monitoring. Running the offline sample does not set those up.
When should you proceed with an offline trial?
You are ready for a trial once you have checked the hardware, chosen the source revision and checkpoint, estimated memory, and decided what to test. Resolve any uncertain hardware support before spending time on the build.
Keep the source commit, device layout, dependency versions and sampling settings with your run logs. Record context length and elapsed time too. Check that the model produces useful output and finishes without errors; a successful build does not prove that inference works.
FAQ
Does DeepInfer officially support consumer RTX cards?
The README lists NVIDIA SM100 GPUs and Ascend 950 NPUs. It does not list support for consumer RTX cards.
Is there one minimum VRAM number for every deployment?
This guide does not give a universal VRAM minimum. Work out memory for your checkpoint, runtime, context length, concurrent requests and parallel settings.
Does the supported model list include all DeepSeek models?
No. The README names DeepSeek-V4.1-Flash. Check the engine's support list even if you already have another DeepSeek model's weights.
Have these deployment instructions been tested on an accelerator here?
No. We checked the documentation but have not built or run DeepInfer on the listed hardware. You will need to test compilation, inference and performance on your machine.
Once the hardware and trial goal are clear, follow the setup guide and check the offline output before working on a public API.