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* feat: add Valkey Search vector store backend Add a new built-in Go gRPC store backend 'valkey-store' that implements the four Stores RPCs (Set/Get/Delete/Find) against the Valkey Search module (FT.*) using the pure-Go github.com/valkey-io/valkey-go client. It is selected via the existing per-request 'backend' field on /stores, so there is no proto or HTTP API change, and it mirrors the in-memory local-store while adding persistence across restarts and opt-in HNSW. Each vector is a Valkey HASH keyed by hex(little-endian float32); the index is created lazily on first Set (FLAT+COSINE by default), cosine similarity is derived as 1-distance, and namespaces get a collision-resistant token. Includes unit tests (valkey-go mock) and env-gated integration tests against valkey/valkey-bundle, plus build/matrix/gallery wiring and docs. Assisted-by: Kiro:claude-opus-4.8 golangci-lint Signed-off-by: Daria Korenieva <daric2612@gmail.com> * Address review feedback: recover persisted index dimension, harden Find - Load now recovers the persisted vector DIM from FT.INFO (not just index existence), so a post-restart Set/Find validates against the real DIM instead of silently re-learning a wrong one and dropping mismatched vectors from the index. This also restores Find's dimension check after a restart. - StoresFind treats a dropped/missing index as an empty store (empty result, no error) and clears the stale indexCreated flag, matching local-store's empty-store behaviour. - StoresSet reuses checkDims for its per-key length check so the four RPCs share one dimension-guard implementation. - Add unit tests for FT.INFO dimension recovery, loadIndexState, and the dropped-index Find path. Assisted-by: Kiro:claude-opus-4.8 Signed-off-by: Daria Korenieva <daric2612@gmail.com> * Address review feedback: TLS ServerName/CA, Find nil-check, config fail-fast Addresses external review comments on the valkey-store backend: - StoresFind now rejects a nil/empty query Key before dereferencing it, so a malformed gRPC request can no longer panic the backend. - TLS: derive ServerName (SNI) from the VALKEY_ADDR host so certificate verification works for IP-addressed endpoints, and add VALKEY_TLS_CA_CERT (custom CA bundle) and VALKEY_TLS_SKIP_VERIFY (testing-only) knobs. - Config integer parsing now fails fast on a malformed value (e.g. VALKEY_HNSW_M=1x6) instead of silently defaulting, matching the fail-fast behaviour of the index-algo/distance-metric validation. - Add VALKEY_DB (SELECT n) support for logical-DB isolation. - Cap the human-readable part of a namespace token at 64 chars so a very long model name cannot produce an unbounded key prefix / index name (the appended short hash keeps distinct namespaces collision-free). - Document the KNN-query injection-safety invariant (fields are constants) and why StoresGet uses a single aggregate DoMulti deadline for reads. - Unit tests for the Find nil/empty-key guard, fail-fast HNSW parsing, and VALKEY_DB parsing/validation; docs + .env updated for the new vars. Assisted-by: Kiro:claude-opus-4.8 golangci-lint Signed-off-by: Daria Korenieva <daric2612@gmail.com> * Address review feedback: configure valkey-store via model config richiejp asked that the valkey-store backend take its configuration from a model config rather than process-wide VALKEY_* environment variables, so multiple stores can each have their own Valkey config within one LocalAI process. This removes every env access from the backend and routes config through the model-config seam every other backend uses. - config.go: loadConfig(opts *pb.ModelOptions) now parses the model config `options:` list (key:value strings, split on the first ':') instead of os.Getenv. Option keys mirror the old VALKEY_* names without the prefix (addr, index_algo, distance_metric, ...). Defaults, fail-fast validation and the mandatory client name are unchanged. - store.go: Load threads opts into loadConfig; TLS comments/errors renamed off the VALKEY_* names. - core/backend/stores.go: StoreBackend and NewVectorStore take a *config.ModelConfigLoader, resolve the per-store ModelConfig by store name, and pass its Options (and Backend when unset) to the backend via WithLoadGRPCLoadModelOpts. No config -> default backend + built-in defaults, preserving the zero-config experience. - Endpoints/routes/application: thread the config loader to StoreBackend. - Unit + integration tests: configure via options; the integration test passes addr through the model-config path (VALKEY_ADDR is now only the test harness locating the server). - docs + .env: document the model-config options, drop the env var table. Assisted-by: Kiro:claude-opus-4.8 Signed-off-by: Daria Korenieva <daric2612@gmail.com> * Remove valkey-store informational comment from .env The backend is configured via model config, not env vars — the comment was unnecessary noise in .env. The configuration is already documented in docs/content/features/stores.md. Signed-off-by: Daria Korenieva <daric2612@gmail.com> * feat(valkey-store): gate Load on NamespacePrefix to refuse autoload probing Mirror local-store's pattern: reject model names without store.NamespacePrefix so the model loader's greedy autoload probe cannot bind an arbitrary model name to the vector store backend (the #9287 failure mode). Also adds unit tests for the gate covering: prefixed namespace, prefix alone, unprefixed model name, empty model, and nil opts. Signed-off-by: Daria Korenieva <daric2612@gmail.com> * feat(valkey-store): add username_env/password_env credential indirection Add support for resolving Valkey credentials from environment variables named in the model config, mirroring cloud-proxy's api_key_env pattern. This keeps secrets out of model YAML files and lets distinct store configs each reference their own credentials. Options: username_env / password_env name the env var holding the value. The direct username / password options still work and take precedence when both are set (backward compatible). Includes 5 unit tests and updated stores.md documentation. Signed-off-by: Daria Korenieva <daric2612@gmail.com> * fix: correct rebase artifacts in backend-matrix.yml and Makefile Fix two issues introduced by the conflict-resolution script during the rebase onto master: 1. .github/backend-matrix.yml: valkey-store entries were merged INTO the cloud-proxy entries (duplicate keys in same YAML map items) instead of being separate list items. This broke cloud-proxy Linux builds and the cloud-proxy darwin entry lost its build-type/lang. Fixed by making them standalone entries and restoring cloud-proxy exactly as on master. 2. Makefile: duplicated .NOTPARALLEL and docker-build-backends lines. Collapsed to single lines that are master's current content plus the valkey-store additions. Also adds the three optional pickups from #10801: - /valkey-store in .gitignore (the built binary) - valkey-store row in docs/content/reference/compatibility-table.md - valkey-store line in backend/README.md Signed-off-by: Daria Korenieva <daric2612@gmail.com> --------- Signed-off-by: Daria Korenieva <daric2612@gmail.com> Co-authored-by: Daria Korenieva <daric2612@gmail.com>
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+++ disableToc = false title = "Model compatibility table" weight = 24 url = "/model-compatibility/" +++
Besides llama based models, LocalAI is compatible also with other architectures. The table below lists all the backends, compatible models families and the associated repository.
{{% notice note %}}
LocalAI will attempt to automatically load models which are not explicitly configured for a specific backend. You can specify the backend to use by configuring a model with a YAML file. See [the advanced section]({{%relref "advanced" %}}) for more details.
All backends listed here can be installed on demand from the [Backend Gallery]({{%relref "features/backends" %}}). The exact set of acceleration variants published for each backend is defined in backend/index.yaml.
{{% /notice %}}
Text Generation & Language Models
| Backend | Description | Capability | Embeddings | Streaming | Acceleration |
|---|---|---|---|---|---|
| llama.cpp | LLM inference in C/C++. Supports LLaMA, Mamba, RWKV, Falcon, Starcoder, GPT-2, and many others | GPT, Functions | yes | yes | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
| ik_llama.cpp | Hard fork of llama.cpp optimized for CPU/hybrid CPU+GPU with IQK quants, custom quant mixes, and MLA for DeepSeek | GPT | yes | yes | CPU (AVX2+) |
| turboquant | llama.cpp fork adding the TurboQuant KV-cache quantization scheme | GPT | yes | yes | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Jetson L4T |
| ds4 | DeepSeek V4 Flash single-model inference engine, optimized for Metal and CUDA | GPT | no | yes | CPU, CUDA 12/13, Metal, Jetson L4T |
| vllm.cpp | From-scratch C++20 port of vLLM by the LocalAI team: paged KV cache, continuous batching, prefix caching, safetensors + GGUF, engine-enforced structured output, no Python at inference | GPT, Functions | no | yes | CPU, CUDA 12/13 (Blackwell-family), Vulkan, Metal, Jetson L4T (GB10) |
| vLLM | Fast LLM serving with PagedAttention; GPTQ/AWQ/FP8 quantization | GPT, Functions, Multimodal | no | yes | CUDA 12/13, ROCm, Intel SYCL, Jetson L4T |
| vLLM Omni | Unified multimodal generation (text, image, video, audio) on top of vLLM | Multimodal GPT, Functions | no | yes | CUDA 12/13, ROCm, Jetson L4T |
| SGLang | Fast serving framework for LLMs and vision-language models with speculative decoding | GPT, Functions, Multimodal | no | yes | CUDA 12/13, ROCm, Intel SYCL, Jetson L4T |
| transformers | HuggingFace Transformers framework | GPT, Embeddings, Multimodal | yes | yes* | CUDA 12/13, ROCm, Intel SYCL, Metal |
| MLX | Apple Silicon LLM inference | GPT, Functions | no | yes | CPU, CUDA 12/13, Metal, Jetson L4T |
| MLX-VLM | Vision-Language Models on Apple Silicon | Multimodal GPT, Functions | no | yes | CPU, CUDA 12/13, Metal, Jetson L4T |
| MLX Distributed | Distributed LLM inference across multiple Apple Silicon Macs | GPT | no | no | CPU, CUDA 12/13, Metal, Jetson L4T |
| tinygrad | Minimalist deep-learning framework with zero runtime dependencies | GPT, Embeddings, Multimodal | yes | yes | CPU |
Speech-to-Text
| Backend | Description | Acceleration |
|---|---|---|
| whisper.cpp | OpenAI Whisper in C/C++ | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
| faster-whisper | Fast Whisper with CTranslate2 | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal, Jetson L4T |
| WhisperX | Word-level timestamps and speaker diarization | CPU, CUDA 12/13, Metal, Jetson L4T |
| moonshine | Ultra-fast transcription for low-end devices (ONNX) | CPU, CUDA 12/13, Metal |
| parakeet.cpp | C++/GGML port of NVIDIA NeMo Parakeet (tdt/ctc/rnnt/hybrid), with cache-aware streaming | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
| CrispASR | Unified speech engine (whisper.cpp fork) supporting Parakeet, Canary, and many ASR architectures, plus TTS | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
| voxtral | Voxtral Realtime 4B speech-to-text in pure C | CPU, Metal |
| Qwen3-ASR | Qwen3 automatic speech recognition | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal, Jetson L4T |
| NeMo | NVIDIA NeMo ASR toolkit | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal |
| sherpa-onnx | Sherpa-ONNX ASR (Whisper, Paraformer, SenseVoice) and TTS | CPU, CUDA 12, Metal |
Text-to-Speech
| Backend | Description | Acceleration |
|---|---|---|
| piper | Fast neural TTS | CPU, Metal |
| Coqui TTS | TTS with 1100+ languages and voice cloning | CUDA 12, ROCm, Intel SYCL, Metal |
| Kokoro | Lightweight TTS (82M params) | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal, Jetson L4T |
| Kokoros | Pure Rust Kokoro TTS via ONNX | CPU |
| Chatterbox | Production-grade TTS with emotion control | CPU, CUDA 12/13, Metal, Jetson L4T |
| VibeVoice | Real-time TTS with voice cloning | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal, Jetson L4T |
| vibevoice.cpp | Native C++/GGML port of VibeVoice for TTS (voice cloning) and long-form ASR with diarization | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
| moss-tts.cpp | Native C++/GGML port of OpenMOSS MOSS-TTS-Local v1.5: 48 kHz stereo TTS with reference-audio voice cloning | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
| magpie-tts.cpp | Native C++/GGML port of NVIDIA Magpie TTS Multilingual 357M: 22.05 kHz mono TTS, 5 baked voices, 9+ languages | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
| Qwen3-TTS | TTS with custom voice, voice design, and voice cloning | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal, Jetson L4T |
| qwentts.cpp | Native C++/GGML Qwen3-TTS with streaming, named speakers, and voice design | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
| OmniVoice | Native C++/GGML TTS with voice cloning, voice design, and streaming | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
| fish-speech | High-quality TTS with voice cloning | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal, Jetson L4T |
| Pocket TTS | Lightweight CPU-efficient TTS with voice cloning | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal, Jetson L4T |
| OuteTTS | TTS with custom speaker voices | CPU, CUDA 12 |
| faster-qwen3-tts | Real-time Qwen3-TTS with CUDA graph capture | CPU, CUDA 12/13, Jetson L4T |
| NeuTTS Air | Instant voice cloning, on-device TTS | CPU, CUDA 12, ROCm |
| VoxCPM | Expressive end-to-end TTS | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal |
| Kitten TTS | Kitten TTS model | CPU, Metal |
| Supertonic | Lightning-fast on-device multilingual TTS via ONNX | CPU |
| MLX-Audio | Audio models on Apple Silicon | CPU, CUDA 12/13, Metal, Jetson L4T |
| liquid-audio | LFM2 end-to-end speech-to-speech, ASR, and TTS | CPU, CUDA 12/13, ROCm, Intel SYCL, Jetson L4T |
Music & Sound Generation
| Backend | Description | Acceleration |
|---|---|---|
| ACE-Step | Music generation from text descriptions, lyrics, or audio | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal |
| acestep.cpp | ACE-Step 1.5 C++ backend using GGML | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
Image & Video Generation
| Backend | Description | Acceleration |
|---|---|---|
| stable-diffusion.cpp | Stable Diffusion, Flux, PhotoMaker, Ideogram in C/C++ | CPU, CUDA 12/13, Intel SYCL, Vulkan, Metal, Jetson L4T |
| diffusers | HuggingFace diffusion models (image and video generation) | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal, Jetson L4T |
| vLLM Omni | Multimodal generation including text-to-image and text-to-video | CUDA 12/13, ROCm, Jetson L4T |
Vision, Detection & Recognition
| Backend | Description | Acceleration |
|---|---|---|
| RF-DETR | Real-time transformer-based object detection (Python) | CPU, CUDA 12/13, Intel SYCL, Metal, Jetson L4T |
| rf-detr.cpp | Native RF-DETR object detection and instance segmentation in C/C++ using GGML | CPU, CUDA 12/13, Intel SYCL, Vulkan, Jetson L4T |
| locate-anything.cpp | Open-vocabulary object detection and visual grounding (LocateAnything-3B) in C/C++ using GGML | CPU, CUDA 12/13, Intel SYCL, Vulkan, Jetson L4T |
| depth-anything.cpp | Depth Anything 3 monocular metric depth + camera pose in C/C++ using GGML | CPU, CUDA 12/13, Intel SYCL, Vulkan, Jetson L4T |
| sam3.cpp | Segment Anything (SAM 3/2/EdgeTAM) with text/point/box prompts in C/C++ using GGML | CPU, CUDA 12/13, Intel SYCL, Vulkan, Jetson L4T |
| face-detect.cpp | Native face detection, recognition, embedding, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace) in C/C++ using GGML | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
| voice-detect.cpp | Native speaker (voice) recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2) in C/C++ using GGML | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
| insightface | Face verification, embedding, and anti-spoofing liveness (ONNX Runtime) | CPU, CUDA 12 |
| speaker-recognition | Speaker (voice) recognition via SpeechBrain ECAPA-TDNN | CPU, CUDA 12, Metal |
Audio Processing
| Backend | Description | Acceleration |
|---|---|---|
| Silero VAD | Voice Activity Detection | CPU, Metal |
| LocalVQE | Joint acoustic echo cancellation, noise suppression, and dereverberation in C/C++ using GGML | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Jetson L4T |
| Opus | Audio codec for WebRTC / Realtime API | CPU, Metal |
Utilities & Other
| Backend | Description | Acceleration |
|---|---|---|
| rerankers | Document reranking for RAG | CUDA 12, ROCm, Intel SYCL, Metal |
| privacy-filter.cpp | Standalone GGML engine for the openai-privacy-filter PII/NER token-classification model family (powers LocalAI's PII redaction tier) | CPU, CUDA 13, Vulkan |
| local-store | Local-first vector database for embeddings | CPU, Metal |
| valkey-store | Durable vector store for embeddings backed by Valkey Search (FLAT or HNSW) | CPU, Metal |
| TRL | Fine-tuning (SFT, DPO, GRPO, RLOO, KTO, ORPO) | CPU, CUDA 12/13 |
| llama.cpp quantization | HuggingFace → GGUF model conversion and quantization | CPU, Metal |
Acceleration Support Summary
GPU Acceleration
- NVIDIA CUDA: CUDA 12.0, CUDA 13.0 support across most backends
- AMD ROCm: HIP-based acceleration for AMD GPUs
- Intel oneAPI: SYCL-based acceleration for Intel GPUs (F16/F32 precision)
- Vulkan: Cross-platform GPU acceleration
- Metal: Apple Silicon GPU acceleration (M1/M2/M3+)
Specialized Hardware
- NVIDIA Jetson (L4T CUDA 12): ARM64 support for embedded AI (AGX Orin, Jetson Nano, Jetson Xavier NX, Jetson AGX Xavier)
- NVIDIA Jetson (L4T CUDA 13): ARM64 support for embedded AI (DGX Spark)
- Apple Silicon: Native Metal acceleration for Mac M1/M2/M3+
- Darwin x86: Intel Mac support
CPU Optimization
- AVX/AVX2/AVX512: Advanced vector extensions for x86
- Quantization: 4-bit, 5-bit, 8-bit integer quantization support
- Mixed Precision: F16/F32 mixed precision support
Note: any backend name listed above can be used in the backend field of the model configuration file (See [the advanced section]({{%relref "advanced" %}})).
- * Only for CUDA and OpenVINO CPU/XPU acceleration.