# DeepSeek V4.1 Flash 1017: Deployment Evidence

The pinned model card and vLLM recipe identify the 1017 artifact and provide configuration starting points. They do not establish its speed on your machine. A useful deployment decision needs the exact model identity, a fully resolved recipe and evidence for the intended hardware and workload.

![Conceptual model card and recipe linked to an empty run-evidence record.](/assets/deepseek-1017-evidence-cover.webp)

*AI-generated conceptual illustration. Configuration documents and independent measurements are separate kinds of evidence.*

## Start with the exact checkpoint

The selected recipe defaults to `deepseek-ai/DeepSeek-V4.1-Flash-1017`. Keep that identity when reading performance reports. A document about an earlier V4.1 checkpoint remains evidence about its original experiment, even if a newer recipe now selects 1017. [Pinned default artifact](https://github.com/vllm-project/recipes/blob/d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94/models/deepseek-ai/DeepSeek-V4.1-Flash.yaml#L114)

Preserve three separate references: the document version you read, the model artifact you intend to load and the runtime version actually executed. Recording a recipe commit alone does not establish the latter two.

## What the publisher recommends

The official card recommends `temperature 1.0` and `top_p 1.0`. It declares a `1M` context and recommends a `max_tokens` budget of at least `256K`. These are publisher settings and limits; they do not demonstrate an allocation on a particular workstation or require every answer to reach that output length. [Sampling](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/blob/1bae0e1088993b9b82de08f59de5be5a34e42170/README.md#L81), [context and output budget](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/blob/1bae0e1088993b9b82de08f59de5be5a34e42170/README.md#L84)

The card supplies a Python encoding reference rather than a Jinja-format chat template. Check how the chosen serving integration handles the model's protocol; that fact alone does not mean every application needs a new custom implementation. [Prompt interface](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/blob/1bae0e1088993b9b82de08f59de5be5a34e42170/README.md#L69)

## Read the recipe's declarations precisely

| Selected declaration | Meaning for planning |
|---|---|
| Model-wide minimum vLLM: `0.31.0` | A documented baseline; check additional variant and hardware requirements |
| Default precision label: `fp8` | A recipe label, not a tensor-by-tensor precision audit |
| Default planning minimum: `614 GB` | A declared planning value for this variant, not measured peak allocation or guaranteed capacity |
| Shared base arguments: `--tokenizer-mode deepseek_v41` | One layer of the configuration, not a complete launch command |
| Default selected features: `spec_decoding`, `text_only` | Configuration selections, not measured acceleration or a full model-capability statement |

Source: [minimum runtime](https://github.com/vllm-project/recipes/blob/d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94/models/deepseek-ai/DeepSeek-V4.1-Flash.yaml#L42), [default variant and selections](https://github.com/vllm-project/recipes/blob/d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94/models/deepseek-ai/DeepSeek-V4.1-Flash.yaml#L114), [planning minimum](https://github.com/vllm-project/recipes/blob/d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94/models/deepseek-ai/DeepSeek-V4.1-Flash.yaml#L125).

## Turn the sources into a deployment decision

Choose an artifact variant and hardware profile before assembling settings. Read the shared, variant, hardware, strategy and optional-feature layers together. A copied fragment can lose the conditions that made the original configuration meaningful.

Use the documents to prepare a reproducible configuration record. A planning value can help reject an unsuitable proposal, but it cannot replace a traceable allocation and successful request on the intended system. Keep advertised context, configured context, tested input length and available serving capacity as separate fields.

## What a useful run report still needs

For evidence that someone actually ran the intended configuration, look for:

- Exact artifact, conversion and runtime identities.
- Device count, memory, topology and complete resolved launch settings.
- Input and output lengths, concurrency and stopping rules.
- Cache, warmup, sampling and speculative-decoding settings.
- Timing definitions, repetitions, failures and uncertainty.

Missing details stay unknown. The indexed sources used for this article do not establish a RunAIHere speed measurement for 1017, and this page does not supply a hardware performance ranking. RunAIHere has not independently executed or benchmarked this checkpoint.


## Original sources and conditions

- deepseek-ai: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/blob/1bae0e1088993b9b82de08f59de5be5a34e42170/README.md
  - Document revision: 1bae0e1088993b9b82de08f59de5be5a34e42170
  - SHA-256: b72b796e4f4c739eca5d0ad360a7679b39118ca7eb64891f7151a527b292c42a
- vllm-project: https://github.com/vllm-project/recipes/blob/d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94/models/deepseek-ai/DeepSeek-V4.1-Flash.yaml
  - Document revision: d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94
  - SHA-256: 8fa89be8c171136cc34460cd1d8d64134573c79ee64245d942a057ee4a4d449e

Checked 2026-10-18. AI-assisted writing, source checked by Run AI Here. No independent model tests. Conditions and selected source facts are in the adjacent JSON export.

```json
{
  "facts": [
    {
      "id": "deepseek-v41-flash-1017-official-fact-0",
      "label": "Recommended sampling",
      "value": "temperature 1.0; top_p 1.0",
      "classification": "publisher_declaration",
      "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/blob/1bae0e1088993b9b82de08f59de5be5a34e42170/README.md#L81",
      "source_document_sha256": "b72b796e4f4c739eca5d0ad360a7679b39118ca7eb64891f7151a527b292c42a"
    },
    {
      "id": "deepseek-v41-flash-1017-official-fact-1",
      "label": "Published context / recommended output budget",
      "value": "1M context; max_tokens at least 256K",
      "classification": "publisher_declaration",
      "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/blob/1bae0e1088993b9b82de08f59de5be5a34e42170/README.md#L84",
      "source_document_sha256": "b72b796e4f4c739eca5d0ad360a7679b39118ca7eb64891f7151a527b292c42a"
    },
    {
      "id": "deepseek-v41-flash-1017-official-fact-2",
      "label": "Prompt interface",
      "value": "Python encoder; no Jinja-format chat template",
      "classification": "publisher_declaration",
      "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/blob/1bae0e1088993b9b82de08f59de5be5a34e42170/README.md#L69",
      "source_document_sha256": "b72b796e4f4c739eca5d0ad360a7679b39118ca7eb64891f7151a527b292c42a"
    },
    {
      "id": "deepseek-v41-flash-vllm-fact-4",
      "label": "Model-wide minimum vLLM",
      "value": "0.31.0",
      "classification": "recipe_declaration",
      "url": "https://github.com/vllm-project/recipes/blob/d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94/models/deepseek-ai/DeepSeek-V4.1-Flash.yaml#L42",
      "source_document_sha256": "8fa89be8c171136cc34460cd1d8d64134573c79ee64245d942a057ee4a4d449e"
    },
    {
      "id": "deepseek-v41-flash-vllm-fact-6",
      "label": "Default artifact",
      "value": "deepseek-ai/DeepSeek-V4.1-Flash-1017",
      "classification": "recipe_declaration",
      "url": "https://github.com/vllm-project/recipes/blob/d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94/models/deepseek-ai/DeepSeek-V4.1-Flash.yaml#L114",
      "source_document_sha256": "8fa89be8c171136cc34460cd1d8d64134573c79ee64245d942a057ee4a4d449e"
    },
    {
      "id": "deepseek-v41-flash-vllm-fact-7",
      "label": "Default precision label",
      "value": "fp8",
      "classification": "recipe_declaration",
      "url": "https://github.com/vllm-project/recipes/blob/d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94/models/deepseek-ai/DeepSeek-V4.1-Flash.yaml#L114",
      "source_document_sha256": "8fa89be8c171136cc34460cd1d8d64134573c79ee64245d942a057ee4a4d449e"
    },
    {
      "id": "deepseek-v41-flash-vllm-fact-8",
      "label": "Default planning minimum (GB; recipe declaration)",
      "value": 614,
      "classification": "recipe_declaration",
      "url": "https://github.com/vllm-project/recipes/blob/d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94/models/deepseek-ai/DeepSeek-V4.1-Flash.yaml#L125",
      "source_document_sha256": "8fa89be8c171136cc34460cd1d8d64134573c79ee64245d942a057ee4a4d449e"
    },
    {
      "id": "deepseek-v41-flash-vllm-fact-10",
      "label": "Shared base arguments (incomplete launch)",
      "value": [
        "--tokenizer-mode",
        "deepseek_v41"
      ],
      "classification": "recipe_declaration",
      "url": "https://github.com/vllm-project/recipes/blob/d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94/models/deepseek-ai/DeepSeek-V4.1-Flash.yaml#L114",
      "source_document_sha256": "8fa89be8c171136cc34460cd1d8d64134573c79ee64245d942a057ee4a4d449e"
    },
    {
      "id": "deepseek-v41-flash-vllm-fact-13",
      "label": "Features selected by default",
      "value": [
        "spec_decoding",
        "text_only"
      ],
      "classification": "recipe_declaration",
      "url": "https://github.com/vllm-project/recipes/blob/d5fc1f55cee70f4e1944f2dd4f7569cb8276ad94/models/deepseek-ai/DeepSeek-V4.1-Flash.yaml#L114",
      "source_document_sha256": "8fa89be8c171136cc34460cd1d8d64134573c79ee64245d942a057ee4a4d449e"
    }
  ],
  "reported_results": [],
  "conditions": {
    "model_artifact": "deepseek-ai/DeepSeek-V4.1-Flash-1017",
    "weight_revision": null,
    "runtime_execution_revision": null,
    "hardware": null,
    "input_tokens": null,
    "output_tokens": null,
    "concurrency": null,
    "cache_state": null,
    "independently_measured": false
  }
}
```