--- title: "DeepSeek-V4.1-Flash | Aguasto" description: "Research reference for DeepSeek-V4.1-Flash: official checkpoint, license and memory considerations. Documented in Aguasto Research; no Aguasto deployment or API availability is represented." canonical: "https://www.aguasto.com/models/deepseek-v4-1-flash" last-updated: "2026-10-13" --- # DeepSeek-V4.1-Flash | Aguasto Canonical HTML: [DeepSeek-V4.1-Flash](https://www.aguasto.com/models/deepseek-v4-1-flash) > Research reference for DeepSeek-V4.1-Flash: official checkpoint, license and memory considerations. Documented in Aguasto Research; no Aguasto deployment or API availability is represented. [Aguasto](https://www.aguasto.com/) [Model Index](https://www.aguasto.com/models) DeepSeek-V4.1-Flash [Resources](https://www.aguasto.com/resources) [GPU Library](https://www.aguasto.com/gpus) [Model Index](https://www.aguasto.com/models) [Inference Stack](https://www.aguasto.com/inference-stack) [Learn](https://www.aguasto.com/learn) [Insights](https://www.aguasto.com/insights) [Research](https://www.aguasto.com/research) [Engineering tools](https://www.aguasto.com/tools) DeepSeek / Open-weight checkpoint reference ## DeepSeek-V4.1-Flash Documented in Aguasto Research. This checkpoint has not been evaluated or deployed by Aguasto and is not available through an Aguasto API. Original Aguasto conceptual illustration. Not manufacturer geometry, an operating Aguasto asset, or a measured benchmark. Aguasto Research documents third-party hardware, models and software. References do not represent Aguasto inventory, evaluated deployments or available inference services. ### Checkpoint identity Official identifier deepseek-ai/DeepSeek-V4.1-Flash[DeepSeek-V4.1-Flash official model card (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) · [DeepSeek-V4.1-Flash checkpoint configuration (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) · [DeepSeek-V4.1-Flash MIT license (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) Architecture Multimodal mixture of experts; causal encoder-decoder with compressed sparse attention and conditional memory[DeepSeek-V4.1-Flash official model card (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) · [DeepSeek-V4.1-Flash checkpoint configuration (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) · [DeepSeek-V4.1-Flash MIT license (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) Total parameters Not verified for this recordPublished rounded counts; total stored weights determine the preliminary footprint, including all MoE experts.[DeepSeek-V4.1-Flash official model card (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) · [DeepSeek-V4.1-Flash checkpoint configuration (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) · [DeepSeek-V4.1-Flash MIT license (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) Active parameters Not verified for this recordActive parameters are a compute-related count, not the model's full stored weight count.[DeepSeek-V4.1-Flash official model card (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) · [DeepSeek-V4.1-Flash checkpoint configuration (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) · [DeepSeek-V4.1-Flash MIT license (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) Modalities text-input, image-input, text-output[DeepSeek-V4.1-Flash official model card (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) · [DeepSeek-V4.1-Flash checkpoint configuration (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) · [DeepSeek-V4.1-Flash MIT license (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) Context 1,048,576 tokensNative configuration where directly verified. Extensions, advertised K-labels and configuration differences are preserved in the notes below.[DeepSeek-V4.1-Flash official model card (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) · [DeepSeek-V4.1-Flash checkpoint configuration (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) · [DeepSeek-V4.1-Flash MIT license (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) Release date Exact release date not independently established in the reviewed artifacts[DeepSeek-V4.1-Flash official model card (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) · [DeepSeek-V4.1-Flash checkpoint configuration (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) · [DeepSeek-V4.1-Flash MIT license (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) Download access Public checkpoint reference[DeepSeek-V4.1-Flash official model card (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) · [DeepSeek-V4.1-Flash checkpoint configuration (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) · [DeepSeek-V4.1-Flash MIT license (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) License MITThe publisher's exact checkpoint LICENSE uses MIT terms. Preserve copyright and permission notices. The checkpoint's license is separate from any hosted API contract, and neither license nor model-card examples establish deployment performance.[DeepSeek-V4.1-Flash official model card (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) · [DeepSeek-V4.1-Flash checkpoint configuration (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) · [DeepSeek-V4.1-Flash MIT license (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) ### Theoretical weight-memory footprint A weight estimate is unavailable without a verified parameter count. This estimate excludes KV cache, activations, quantization metadata, non-weight buffers and serving overhead. It is not a tested GPU fit. MoE routing activates only part of the network per token while the complete expert weights still need storage. [Explore the memory estimator](https://www.aguasto.com/tools) #### Quantization The checkpoint declares FP8 quantization with 32x32 blocks, UE8M0 scales and FP4 experts, plus a BF16 general dtype. Weight, activation and cache formats are distinct. A generic all-FP8 or all-four-bit storage estimate would omit those distinctions. [DeepSeek-V4.1-Flash official model card (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) · [DeepSeek-V4.1-Flash checkpoint configuration (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) · [DeepSeek-V4.1-Flash MIT license (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) ### Serving considerations - The publisher separately reports a 552B backbone and 196B conditional-memory component. Activated work differs by phase: the card reports 8B in prefill and 16B in decode. A single total or active count is left unknown until all checkpoint components are reconciled. - The text configuration sets 1,048,576 positions with explicit scaling from a shorter original context. Long-context memory cannot be modeled with a plain attention-cache formula without accounting for its causal encoder-decoder, compressed sparse attention and conditional memory. - The publisher provides a minimal-inference implementation and checkpoint conversion instructions. No vLLM/SGLang support is asserted here from Hugging Face's automatically generated deployment snippets; an exact maintainer-supported engine path remains a separate verification task. - For a future evaluation, pin the artifact, conversion path, quantization kernels, image processing and phase-sensitive cache settings. Measure complete requests under representative prompts; publisher cache-size claims are not Aguasto measurements. - The model is a research reference. Aguasto has not run its deployment commands, verified GPU fit, hosted its weights or offered it through an API. [DeepSeek-V4.1-Flash official model card (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) · [DeepSeek-V4.1-Flash checkpoint configuration (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) · [DeepSeek-V4.1-Flash MIT license (external source)](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) #### Framework documentation These are third-party documentation starting points. Support depends on the exact checkpoint, runtime version and hardware. ### Continue the research [Compare model references](https://www.aguasto.com/models/compare) [GPU specification library](https://www.aguasto.com/gpus) [Weights, cache and serving overhead](https://www.aguasto.com/learn/model-weights-and-runtime-memory) ### Sources & verification The register preserves each source's publication and verification date. Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production. - [DeepSeek-V4.1-Flash official model card ↗](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) Exact checkpoint identity and open weight listing; 552B backbone and196B conditional-memory boundaries; 8B prefill versus16B decode activation statements; Multimodal causal encoder-decoder, compressed sparse attention; Publisher minimal-inference path. Publication: date not stated in the reviewed source. Verified: 2026-10-13. - [DeepSeek-V4.1-Flash checkpoint configuration ↗](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/config.json) DeepseekV41ForCausalLM identity; 1048576 text position limit and scaling; FP8 32x32 blocks, UE8M0 scales, FP4 expert dtype; Vision and text configuration boundaries. Publication: date not stated in the reviewed source. Verified: 2026-10-13. - [DeepSeek-V4.1-Flash MIT license ↗](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE) Exact checkpoint MIT permission and notice terms. Publication: date not stated in the reviewed source. Verified: 2026-10-13. ### Reference journey [Compare models ↗](https://www.aguasto.com/models/compare) [Estimate weights ↗](https://www.aguasto.com/tools) [Hardware references ↗](https://www.aguasto.com/gpus) [Aguasto inference strategy ↗](https://www.aguasto.com/ai-inference) [Sources & verification ↓](https://www.aguasto.com/models/deepseek-v4-1-flash#sources) Research reference · No available Aguasto service ## Company navigation - [Home](https://www.aguasto.com/) - [Infrastructure](https://www.aguasto.com/infrastructure) - [GPU Cloud](https://www.aguasto.com/gpu-cloud) - [AI Inference](https://www.aguasto.com/ai-inference) - [Development & Investment](https://www.aguasto.com/development) - [Company](https://www.aguasto.com/company) - [Contact](https://www.aguasto.com/contact) - [Privacy](https://www.aguasto.com/privacy) - [Resources](https://www.aguasto.com/resources) - [GPU Library](https://www.aguasto.com/gpus) - [Model Index](https://www.aguasto.com/models) - [Inference Stack](https://www.aguasto.com/inference-stack) - [Learn](https://www.aguasto.com/learn) - [Insights](https://www.aguasto.com/insights) - [Research](https://www.aguasto.com/research) - [Industry cases](https://www.aguasto.com/research/cases) - [Why Modular](https://www.aguasto.com/why-modular) - [Partners](https://www.aguasto.com/partners) - [GPU comparison](https://www.aguasto.com/gpus/compare) - [Model comparison](https://www.aguasto.com/models/compare) - [Engineering tools](https://www.aguasto.com/tools) - [Development friction](https://www.aguasto.com/research/hyperscale-development-friction) - [NVIDIA H100 SXM](https://www.aguasto.com/gpus/h100) - [NVIDIA H200 SXM](https://www.aguasto.com/gpus/h200) - [NVIDIA B200 SXM](https://www.aguasto.com/gpus/b200) - [NVIDIA B300](https://www.aguasto.com/gpus/b300) - [NVIDIA GB200 NVL72](https://www.aguasto.com/gpus/gb200-nvl72) - [NVIDIA GB300 NVL72](https://www.aguasto.com/gpus/gb300-nvl72) - [NVIDIA Vera Rubin NVL72](https://www.aguasto.com/gpus/vera-rubin-nvl72) - [AMD Instinct MI300X](https://www.aguasto.com/gpus/mi300x) - [AMD Instinct MI325X](https://www.aguasto.com/gpus/mi325x) - [AMD Instinct MI355X](https://www.aguasto.com/gpus/mi355x) - [AMD Instinct MI455X](https://www.aguasto.com/gpus/mi455x) - [AMD Helios](https://www.aguasto.com/gpus/helios) - [GLM-4.5](https://www.aguasto.com/models/glm-4-5) - [Qwen3-235B-A22B](https://www.aguasto.com/models/qwen3-235b-a22b) - [DeepSeek-R1](https://www.aguasto.com/models/deepseek-r1) - [Llama 3.3 70B Instruct](https://www.aguasto.com/models/llama-3-3-70b-instruct) - [Gemma 3 27B IT](https://www.aguasto.com/models/gemma-3-27b-it) - [gpt-oss-120b](https://www.aguasto.com/models/gpt-oss-120b) - [Kimi-K2-Instruct](https://www.aguasto.com/models/kimi-k2-instruct) - [GLM-5.3](https://www.aguasto.com/models/glm-5-3) - [Qwen3.8-27B](https://www.aguasto.com/models/qwen3-8-27b) - [DeepSeek-V4.1-Flash](https://www.aguasto.com/models/deepseek-v4-1-flash) - [Llama 4 Scout 17B-16E Instruct](https://www.aguasto.com/models/llama-4-scout-17b-16e-instruct) - [Gemma 4 31B IT](https://www.aguasto.com/models/gemma-4-31b-it) - [Kimi K3](https://www.aguasto.com/models/kimi-k3) - [vLLM](https://www.aguasto.com/inference-stack/vllm) - [SGLang](https://www.aguasto.com/inference-stack/sglang) - [TensorRT-LLM](https://www.aguasto.com/inference-stack/tensorrt-llm) - [NVIDIA Dynamo](https://www.aguasto.com/inference-stack/dynamo) - [NVIDIA Triton Inference Server](https://www.aguasto.com/inference-stack/triton) - [AMD ROCm](https://www.aguasto.com/inference-stack/rocm) - [Ray Serve](https://www.aguasto.com/inference-stack/ray-serve) - [Kubernetes GPU orchestration — NVIDIA device plugin](https://www.aguasto.com/inference-stack/kubernetes) - [Model weights are only the beginning of the memory budget](https://www.aguasto.com/learn/model-weights-and-runtime-memory) - [Why tokens per second is not enough to benchmark inference](https://www.aguasto.com/research/inference-benchmark-methodology) - [Prefill, decode and the cost of separating them](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving) - [Choosing between vLLM, SGLang and TensorRT-LLM](https://www.aguasto.com/learn/choosing-an-inference-serving-framework) - [From Power to Inference—Building a Distributed AI Compute Network](https://www.aguasto.com/learn/from-power-to-inference) - [Dedicated compute and shared inference: compare the whole workload](https://www.aguasto.com/insights/dedicated-and-shared-inference-economics) - [Distributed inference reliability begins with the failure boundary](https://www.aguasto.com/learn/distributed-inference-reliability) - [Core Scientific: reading contracted, energized and billed capacity](https://www.aguasto.com/research/cases/core-scientific-capacity-disclosures) - [Why Modular AI Infrastructure Matters Now](https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now) - [What Is a Modular AI Data Center?](https://www.aguasto.com/learn/what-is-a-modular-ai-data-center) - [How Modular AI Data Centers Are Built and Commissioned](https://www.aguasto.com/learn/modular-data-center-factory-to-commissioning) - [Liquid Cooling for High-Density GPU Systems: Follow the Interfaces](https://www.aguasto.com/learn/liquid-cooling-gpu-rack-interfaces) - [Why Electricity Availability Shapes AI Infrastructure Deployment](https://www.aguasto.com/research/electricity-availability-and-time-to-power) - [What Is KV Cache? Capacity, Reuse and Isolation](https://www.aguasto.com/learn/kv-cache-capacity-and-policy) - [Continuous Batching: Throughput Without Hiding Latency](https://www.aguasto.com/learn/continuous-batching-and-latency) - [Applied Digital: Reading the Polaris Forge 1 Delivery Milestones](https://www.aguasto.com/research/cases/applied-digital-polaris-forge-capacity-milestones) - [Crusoe Abilene: Separate Delivered Infrastructure from Expansion Plans](https://www.aguasto.com/research/cases/crusoe-abilene-delivery-and-expansion) - [Official LinkedIn](https://www.linkedin.com/company/aguasto) This is a generated representation of the public HTML page. Diagrams are conceptual. GPU and inference services are planned and are not currently available to book or call. The contact page describes the available inquiry flow.