---
title: "Virginia Tech ARC LLM Gateway free inference"
description: "A recurring no-cost API allowance covering 14 cataloged models."
canonical_url: "https://freeinferencing.com/provider/virginia_tech_arc_llm_api/"
md_url: "https://freeinferencing.com/provider/virginia_tech_arc_llm_api.md"
last_updated: "2026-09-19"
---

# Virginia Tech ARC LLM Gateway

> A recurring no-cost API allowance covering 14 cataloged models.

## Classification

- **Directory:** Current
- **Free access:** Always-free quota
- **Status:** Restricted access
- **Confidence:** High
- **Payment card:** No
- **Account:** Required
- **Equivalent paid value:** Not quantifiable
- **API endpoint:** `https://llm-api.arc.vt.edu/api/v1`

## Models mentioned

- `gpt-oss-120b`
- `GLM-5.3`
- `Kimi-K3`
- `DeepSeek-V4.1-Flash`
- `gpt-oss-120b-thinking-low`
- `gpt-oss-120b-thinking-high`
- `GLM-5.3-thinking-high`
- `Kimi-K3-thinking-low`
- `Kimi-K3-thinking-high`
- `DeepSeek-V4.1-Flash-thinking-low`
- `DeepSeek-V4.1-Flash-thinking-max`
- `Qwen3-Embedding-4B`
- `Qwen/Qwen-Image-2512`
- `Qwen/Qwen-Image-Edit-2511`

## Limits and terms

```yaml
max_nonstreaming_output_tokens: 8000
model_concurrency:
  gpt-oss-120b: 10
  GLM-5.3: 4
  Kimi-K3: 3
  DeepSeek-V4.1-Flash: 10
embedding_tokens_refill_per_minute: 150000
embedding_bucket_tokens: 300000
embedding_concurrent_requests_per_user: 4
```

## What happens to your prompts?

**Not private.** The university-hosted service says all user interactions are logged and preserved, without a published maximum content-retention period.

Privacy is audited separately from price. Review the current governing terms before sending sensitive or regulated data.

## Data governance and agreements

```yaml
data_governance:
  review_status: reviewed
  plan_scope: Virginia Tech ARC university-hosted LLM, embedding, image, and
    OpenWebUI API services.
  prompt_retention: The official ARC page says all user interactions are logged
    and preserved; no maximum prompt-retention period is published.
  response_retention: Responses are part of the preserved interaction record,
    without a published maximum TTL.
  ordinary_logging: Interaction, usage, identity, security, and operational
    records are maintained under Virginia Tech IT Security Office policies.
  model_training: No public commitment excludes preserved interactions from future
    training or evaluation use.
  product_improvement: not_documented
  human_or_operator_access: Authorized university administrators can access
    preserved interactions under institutional security and acceptable-use
    controls.
  subprocessors_and_routing: Core inference runs on Virginia Tech infrastructure
    and the page says data does not leave the university.
  deletion_controls: No per-interaction deletion control or automatic
    content-expiry schedule is published.
  caveat: On-premises processing and regulated-data approvals do not offset the
    explicit preservation of every interaction; DFARS, ITAR, and some regulated
    workloads require separate review.
```

## Eligibility

```yaml
account_required: true
eligible_users: All Virginia Tech students, faculty, and staff
separate_ARC_account_required: false
payment_method_required: false
```

## API compatibility and modalities

- **Compatibility:** OpenAI Chat, Anthropic Messages, OpenAI Embeddings, OpenAI Images, Openwebui Files And RAG
- **Modalities:** Text generation, Embeddings, Image / vision
- **Geography:** United States

## Before you build with Virginia Tech ARC LLM Gateway

### Read the classification narrowly

This record describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every account and region. “Always-free quota” should be interpreted together with the Current directory placement, High confidence, No payment-card status, and the plan-specific sources below.

### Resolve the live model route

The snapshot records 14 model IDs. Match the exact ID against the provider's current catalog before using it in code because zero-price routes, aliases, context limits, and feature support can rotate while an older documentation page remains online.

### Match the quota to the workload shape

Translate the allowance into peak requests per minute, input and output tokens, concurrency, retries, and every daily or monthly ceiling that applies to the intended account. The first limit reached by the workload is the practical ceiling. A large token pool can still fail interactive bursts, and a high request limit can still fail long-context work. Include tool calls and retry traffic, test the largest realistic payload, and treat research, experimental, and community access as conditional on their eligibility and fair-use terms.

### Preserve billing and privacy boundaries

The recorded payment-card requirement is No, and account access is required. Confirm both in the live signup flow, set provider-side budgets when charges are possible, and observe the balance or usage fields after a complete request. The prompt-handling classification is **Not private** because The university-hosted service says all user interactions are logged and preserved, without a published maximum content-retention period. Re-read the terms for the exact route and plan before sending sensitive, regulated, or proprietary content.

### Follow the evidence, then re-check it

This record links 3 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest and most specific governing document or live catalog when sources disagree. A dated finding can become stale even when the page remains online, so retain the source and snapshot that supported the decision and submit a correction when a provider changes a material term.

### Plan fallback without policy drift

A fallback should preserve modality, context length, streaming, structured output, tools, safety controls, and data terms, not only API syntax. Decide which errors may retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. Rotating aliases and free pools can change behavior without changing the endpoint, so retain the response model field and test at least one substitute route before the primary offer becomes unavailable.

### Monitor the offer as a dependency

Capture the model ID, response model, rate-limit headers, usage fields, latency, HTTP status, and provider request identifier. Watch authorization failure, quota exhaustion, catalog removal, policy revision, and balance movement as separate failure modes. Re-check the live catalog and governing sources on a schedule proportionate to the workload's importance, and keep an owner and exit path for any production dependency on volatile free capacity.

### Test one complete request before scaling

Start with the smallest permitted request using the exact credential, model ID, endpoint, region, and account type intended for deployment. Record the status, headers, usage fields, response model, latency, and dashboard balance movement. Then exercise an invalid model, quota exhaustion, or rate limit so failure is explicit and cannot silently switch to a paid route. Validate streaming, structured output, and tool calls separately because a free model can expose fewer features than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and do not send sensitive data until the observed route matches the reviewed agreement.

## Primary sources

- [Virginia Tech AI tools](https://ai.vt.edu/tools.html): Official Product
- [ARC LLM API](https://www.docs.arc.vt.edu/ai/011_llm_api_arc_vt_edu.html): Official Docs
- [Coding agents](https://www.docs.arc.vt.edu/ai/040_coding_agents.html): Official Docs

## Sitemap

See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.