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Available models: Azure Local Foundry
One of three availability references
This page covers Azure Local Foundry, cluster-scale inference on Azure Local. The other two targets carry different rosters: Azure AI Foundry (hosted cloud) and Foundry Local (a runtime inside your own application). Compare all three on Deployment targets.
Everything here is drawn from SPIKE-22, which read the published catalog snapshot dated 2026-07-28, with GPU figures confirmed against Microsoft's published vLLM reference.
Nothing here is deployed
This target has no deployment behind it in this repository, so no row on this page is deployed. Everything in the catalog and deployable in principle is available. This is an availability reference, not an as-built record.
What Azure Local Foundry is, precisely
An Azure Arc-enabled Kubernetes extension, not a device runtime. An operator control plane watches cluster state and reconciles model resources through declarative custom resources (Model and ModelDeployment CRDs).
| Deployment | Arc extension, or Helm |
| Availability | Preview, by request. Submit the access request form before planning around it |
| Runtimes | ONNX-GenAI (CPU or GPU) and vLLM (GPU only, high throughput) |
| API | OpenAI-compatible: /v1/chat/completions generative, /v1/predict predictive |
| Azure subscription | Required, plus Entra permissions and a supported region |
| Auth | API keys or Microsoft Entra ID, TLS-terminated gateway |
| Disconnected | Supported, with a deployment model consistent with connected scenarios |
It also supports bring-your-own models containerized with ONNX or vLLM, including Hugging Face models and fine-tuned variants. Predictive workloads support BYO models only; catalog models cannot serve predictive tasks.
What this target does not have
Text, reasoning, code, speech-to-text, and vision input. Confirmed absent (SPIKE-22 Q5):
| Capability | Available on Azure Local Foundry |
|---|---|
| Image generation | No |
| Text to speech | No |
| Embeddings | No |
| Video generation | No |
| Vision input | Yes - and this is exclusive to this target |
Nothing in this project's cloud catalog runs here.
Two notes before you read the tables
Sizes and licences are mostly UNKNOWN, deliberately. The published catalog snapshot carries no size column, and licence values are published for exactly one model. Resolving either is a query against a running cluster, not a research question. This repository marks unverifiable facts UNKNOWN rather than inventing them.
Publisher reads Microsoft on every ONNX row. That field means "publisher of the ONNX build," not the originating lab.
What each family is actually for
The alias tells you the size, the vendor, and often the quantization. It does not tell you what the model is good at. This section does.
| Family | Reach for it when | Avoid it when |
|---|---|---|
| Phi (Microsoft) | You want the best quality per gigabyte of GPU. phi-4-mini-instruct fits in 7.8 GB with a 93,520 context. phi-4-reasoning is cluster-exclusive and the choice for multi-step working. | You need broad world knowledge or very long documents. |
| gpt-oss-20b / 120b (OpenAI, open weight) | Strong function calling, structured output, and reasoning. gpt-oss-20b is Microsoft's recommended model for Agentic Retrieval. 120b when quality outranks cost. | Your GPUs are Ampere. See the GPU warning below, and note Microsoft says gpt-oss-20b "requires its own GPU". |
| DeepSeek R1 distills | Reasoning-heavy analysis where you want visible working, at 1.5B/7B/14B so you can size to the node. | Latency matters: reasoning models emit far more tokens before answering. |
| DeepSeek V3 line | Frontier-class open-weight quality on-premises. Cluster-exclusive. | Your cluster is small. These are very large and will dominate a node's memory. |
| Qwen (Alibaba) | Multilingual work, and precise size selection from 0.6B to 32B. qwen3-32b is cluster-exclusive. | Licence certainty matters and you have not checked. |
| Qwen Coder | Code generation and developer tooling, 0.5B to 14B. | General chat: these are worse at prose than same-size siblings. |
| Mistral dense | General chat with widely-adopted open models, scaling from 7B to mistral-large-3-675b, the largest entry in the catalog. | Memory is tight. mistral-7b-v0.2 alone needs 15.64 GB, roughly double Phi-4-mini. |
| Mistral MoE and specialist | Mixture-of-experts throughput (mixtral), agentic coding (devstral), maths (mathstral), reasoning (magistral, ministral-3-14b-reasoning). | You have left vllm.modelCacheStorageGi at its 100 GiB default: Microsoft names magistral as a model that exceeds it. |
| Pixtral, Nemotron VL | Vision input. Document understanding, screenshots, diagrams, image Q and A. These are the only vision-capable models on any on-premises target. | You only need text. Vision models cost more memory for no benefit. |
| Nemotron general and omni (NVIDIA) | High-throughput GPU serving, tuned for concurrency rather than single-request latency. Quantization is in the alias: bf16 quality, fp8 balance, nvfp4 smallest. | You are on non-NVIDIA hardware or older generations. |
| Nemotron code / math / reasoning | Narrow, deep specialisms: competitive coding (opencodereasoning, -ioi), terminal and tool use (nemotron-terminal), mathematics (openmath, acemath), reasoning (openreasoning, acereason). | You want one model for everything. These are deliberately narrow. |
| OLMo, SmolLM | Fully-open provenance (OLMo, Allen Institute; olmo-3.1-32b is cluster-exclusive) or the smallest viable footprint (SmolLM3, 3B). | You need frontier quality. |
| Whisper (ASR) | Speech to text. -tiny and -base for throughput, -large-v3-turbo for accuracy. | You want text to speech. Nothing on this target does TTS. |
| Nemotron streaming ASR | Live transcription where Whisper's batch shape adds latency. | You need languages beyond English. |
Start here
phi-4-mini-instruct for general use, qwen2.5-coder-7b for code, gpt-oss-20b if you are building Agentic Retrieval and have the GPU for it, pixtral-12b-2409 if you need to read images, whisper-large-v3-turbo for transcription.
Section 1: the ONNX roster
35 aliases, 70 catalog entries, served by the ONNX-GenAI runtime on CPU or GPU. Every model in this table also runs on Foundry Local; this is the shared core the two on-premises targets have in common.
| Model (alias) | Publisher / origin | Runtime | Notes | Status |
|---|---|---|---|---|
phi-4-mini | Microsoft / Microsoft | onnx-genai, cpu or gpu | Also a vLLM entry; MIT licence; 7.806 GB GPU memory on vLLM | available |
phi-4-mini-reasoning | Microsoft / Microsoft | onnx-genai | Also a vLLM entry; 7.806 GB GPU memory | available |
phi-4 | Microsoft / Microsoft | onnx-genai | Also a vLLM entry | available |
phi-3.5-mini | Microsoft / Microsoft | onnx-genai | Also a vLLM entry; 8.428 GB GPU memory | available |
phi-3-mini-4k | Microsoft / Microsoft | onnx-genai | 3.8B params | available |
phi-3-mini-128k | Microsoft / Microsoft | onnx-genai | 3.8B params | available |
gpt-oss-20b | Microsoft / OpenAI (open weight) | onnx-genai | Also a vLLM entry; see the GPU warning below | available |
qwen3-0.6b / -1.7b / -4b / -8b / -14b | Microsoft / Alibaba | onnx-genai | qwen3-4b has CPU/GPU version skew | available |
qwen3.5-2b-text | Microsoft / Alibaba | onnx-genai | available | |
qwen2.5-0.5b / -1.5b / -7b / -14b | Microsoft / Alibaba | onnx-genai | Also vLLM entries | available |
qwen2.5-coder-0.5b / -1.5b / -7b / -14b | Microsoft / Alibaba | onnx-genai | Also vLLM entries | available |
deepseek-r1-7b / -14b | Microsoft / DeepSeek | onnx-genai | Distilled Qwen; also vLLM entries | available |
mistral-7b-v0.2 | Microsoft / Mistral AI | onnx-genai | Also a vLLM entry; 15.64 GB GPU memory | available |
mistral-nemo-12b-instruct | Microsoft / Mistral AI and NVIDIA | onnx-genai | available | |
olmo-3-7b-instruct | Microsoft / Allen Institute for AI | onnx-genai | available | |
smollm3-3b | Microsoft / Hugging Face | onnx-genai | available | |
whisper-tiny / -base / -small / -medium / -large-v3-turbo | Microsoft / OpenAI | onnx-genai | task automatic-speech-recognition | available (ASR) |
nemotron-speech-streaming-en-0.6b / -es-0.6b | Microsoft / NVIDIA | onnx-genai | 0.6B params | available (ASR) |
nemotron-3.5-asr-streaming-0.6b | Microsoft / NVIDIA | onnx-genai | 0.6B params | available (ASR) |
Section 2: the vLLM roster, exclusive to this target
100 entries, GPU only, and not available on Foundry Local at all: vLLM is a GPU-only container runtime with no device-SDK equivalent. Grouped by family, with every alias named. Framework is vllm and task is chat-completion except where noted. Size, RAM, and licence are UNKNOWN except where GPU memory is published.
| Family | Aliases | Notes | Status |
|---|---|---|---|
| Microsoft Phi | phi-4, phi-4-reasoning, phi-4-mini-instruct, phi-4-mini-reasoning, phi-3.5-mini-instruct | phi-4-reasoning has no ONNX entry, so it is exclusive to this target | available |
| OpenAI open weight | gpt-oss-20b, gpt-oss-120b | gpt-oss-120b is exclusive to this target. gpt-oss-20b is Microsoft's recommended model for Agentic Retrieval and "requires its own GPU" | available |
| DeepSeek | deepseek-r1-distill-qwen-1.5b, -7b, -14b, deepseek-v3-0324, deepseek-v3.1, deepseek-v3.2, deepseek-v3.2-speciale | The V3 line is exclusive to this target and very large | available |
| Qwen | qwen2.5-0.5b-instruct, -1.5b-instruct, -7b-instruct, -14b-instruct, qwen2.5-coder-0.5b/1.5b/7b/14b-instruct, qwen3-0.6b, -1.7b, -8b, -14b, -32b | qwen3-32b is exclusive to this target | available |
| Mistral AI, dense | mistral-7b-instruct-v0.2, -v0.3, mistral-nemo-instruct-2407, mistral-nemo-instruct-fp8-2407, mistral-small-24b-instruct-2501, mistral-small-3.1-24b-instruct-2503, mistral-small-3.2-24b-instruct-2506, mistral-small-4-119b-2603, mistral-small-4-119b-2603-nvfp4, mistral-large-3-675b-instruct-2512 | mistral-large-3-675b is the largest entry in the catalog | available |
| Mistral AI, MoE and specialist | mixtral-8x7b-instruct-v0.1, mixtral-8x22b-instruct-v0.1, magistral-small-2506, -2507, -2509, devstral-small-2505, -2507, mathstral-7b-v0.1, ministral-3-3b-instruct-2512, ministral-3-8b-instruct-2512, ministral-3-14b-reasoning-2512 | Microsoft names magistral as a model that exceeds the 100 GiB vllm.modelCacheStorageGi default | available |
| Mistral AI, vision | pixtral-12b-2409 | Vision-language | available |
| NVIDIA Nemotron, vision | nemotron-nano-12b-v2-vl-bf16, -fp8, -nvfp4-qad | Vision-language | available |
| NVIDIA Nemotron, omni | nemotron-3-nano-omni-30b-a3b-reasoning-bf16, -fp8, -nvfp4 | Multi-modal by name; capability surface not documented | available |
| NVIDIA Nemotron, general | nemotron-nano-9b-v2, -fp8, -nvfp4, -japanese, nemotron-nano-12b-v2, nemotron-3-nano-4b-bf16, -fp8, nemotron-3-nano-30b-a3b-bf16, -nvfp4, nemotron-3-super-120b-a12b-bf16, -fp8, -nvfp4, nemotron-4-mini-hindi-4b-instruct | Quantization is encoded in the alias | available |
| NVIDIA Nemotron, code | opencodereasoning-nemotron-7b, -14b, -32b, -32b-ioi, opencodereasoning-nemotron-1.1-7b, -1.1-14b, -1.1-32b, nemotron-terminal-8b, -14b, -32b | available | |
| NVIDIA Nemotron, math and reasoning | openmath-nemotron-1.5b, -7b, -14b, -14b-kaggle, -32b, openreasoning-nemotron-1.5b, -7b, -14b, -32b, acereason-nemotron-7b, -14b, acereason-nemotron-1.1-7b, acemath-rl-nemotron-7b | available | |
| Allen Institute for AI | olmo-3-7b-instruct, olmo-3.1-32b-instruct | olmo-3.1-32b is exclusive to this target | available |
| Hugging Face | smollm3-3b | available | |
| OpenAI Whisper (ASR) | whisper-tiny, whisper-base, whisper-small, whisper-medium, whisper-large-v3-turbo | task automatic-speech-recognition | available (ASR) |
| NVIDIA streaming ASR | nemotron-speech-streaming-en-0.6b | task automatic-speech-recognition | available (ASR) |
Vision input is exclusive to this target. pixtral-12b-2409 and the three nemotron-nano-12b-v2-vl variants are vision-language models and appear only here. Foundry Local has no vision-capable entry.
Published GPU requirements: size the cluster from these
These five are the only entries Microsoft publishes hard figures for. Read the GPU generation column before sizing anything.
| Model | Max context | Recommended minimum GPU | Required GPU memory | GPU utilization |
|---|---|---|---|---|
| Phi-3.5-mini-instruct | 29,472 | Ampere (CC 8.0)+ | 8.428 GB | 0.85 |
| Phi-4-mini-instruct | 93,520 | Ampere (CC 8.0)+ | 7.806 GB | 0.85 |
| Phi-4-mini-reasoning | 93,520 | Ampere (CC 8.0)+ | 7.806 GB | 0.85 |
| Mistral-7B-Instruct-v0.2 | 29,328 | Ampere (CC 8.0)+ | 15.64 GB | 0.85 |
| gpt-oss-20b | 96,784 | Blackwell (CC 10.0)+ | 14.793 GB | 0.8 |
gpt-oss-20b is two GPU generations ahead of everything else
Every other documented model recommends Ampere (CC 8.0). gpt-oss-20b recommends Blackwell (CC 10.0), and it is also the model Microsoft recommends for Agentic Retrieval. Sizing a cluster from the other four rows and adding gpt-oss-20b later is an expensive surprise. Minimum supported across all five is Volta (CC 7.0), but minimum supported is not the same as usable.
The authoritative list is your cluster, not this page
No published Microsoft page is a complete catalog. concept-models shows five models and calls them "representative examples only"; concept-model-catalog shows twelve and says the table "isn't exhaustive." The two disagree in scope for the same product. Ask the cluster:
bash
kubectl get configmap foundry-local-catalog -n foundry-local-operator \
-o jsonpath="{.data['catalog\.json']}" | ConvertFrom-Json |
Select-Object -ExpandProperty models |
Format-Table alias, displayName, task, frameworkOr through the REST API, with port forwarding to inference-operator-api:
bash
curl -k -s https://localhost:8080/api/v1/models -H "Authorization: Bearer $token"A capability read from documentation is a claim; exercised against the cluster it is a fact.
Keeping this current
This page is a transcription of a catalog snapshot dated 2026-07-28. It will drift, and the product is in preview under active deployment. Generating it from the live catalog is tracked as a feature request: see issue #15, auto-refresh the model availability catalogs.
See also
- Model availability matrix, all three targets side by side, filterable.
- Available models: Azure AI Foundry, the hosted cloud roster.
- Available models: Foundry Local, the in-application runtime roster.
- SPIKE-22, the research behind every row here, including the open unknowns.
- Model catalog, what this project chose and why.
- Model selection, the methodology behind the choosing.
- Azure Local Foundry: models.