# mlx-vlm | Prince Canuma | Knowledge Base | Instagit

MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.

GitHub Stars: 3.7k

Repository: https://github.com/Blaizzy/mlx-vlm

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## Articles

### [How to Troubleshoot Common MLX-VLM Issues: A Complete Guide](/Blaizzy/mlx-vlm/troubleshoot-common-mlx-vlm-issues)

Troubleshoot common MLX-VLM errors with this complete guide. Resolve runtime issues by validating inputs, flags, and model configurations for smooth operation.

- Tags: how-to-guide
- Published: 2026-04-05

### [How to Integrate MLX-VLM into Custom Applications: A Complete Developer's Guide](/Blaizzy/mlx-vlm/integrate-mlx-vlm-custom-applications)

Integrate MLX-VLM into custom applications with this developer's guide. Learn to load models, prepare inputs, and generate outputs for Vision-Language inference on Apple Silicon using Python.

- Tags: how-to-guide
- Published: 2026-04-05

### [Which Models Are Supported by MLX-VLM? Complete List and Usage Guide](/Blaizzy/mlx-vlm/supported-models-mlx-vlm)

Discover which models MLX-VLM supports including Qwen2-VL, Qwen2.5-VL, Idefics3, and LLaVA. Get the complete list and usage guide for vision-language tasks on Apple Silicon.

- Tags: api-reference
- Published: 2026-04-05

### [TurboQuant Performance Benefits: Memory Reduction and Speed Optimization in MLX-VLM](/Blaizzy/mlx-vlm/turboquant-performance-benefits)

Discover TurboQuant's performance benefits, reducing MLX-VLM memory usage by up to 76% and accelerating attention computation with custom Metal kernels.

- Tags: performance
- Published: 2026-04-05

### [How to Configure TurboQuant for the MLX-VLM Server: Complete Setup Guide](/Blaizzy/mlx-vlm/configure-turboquant-mlx-vlm-server)

Learn how to configure TurboQuant for the MLX-VLM server. Set non-integer bit-width and quantization scheme for optimal performance. Complete setup guide.

- Tags: how-to-guide
- Published: 2026-04-05

### [How to Use TurboQuant with the MLX-VLM Python API: A Complete Guide](/Blaizzy/mlx-vlm/use-turboquant-mlx-vlm-python-api)

Learn how to use TurboQuant with the MLX-VLM Python API to compress KV-caches to 2-4 bits per dimension. This guide shows you how to leverage fractional kv_bits or kv_quant_scheme turboquant for efficient model performance.

- Tags: how-to-guide
- Published: 2026-04-05

### [How to Use TurboQuant with the MLX-VLM CLI: KV-Cache Compression Guide](/Blaizzy/mlx-vlm/use-turboquant-mlx-vlm-cli)

Learn to use TurboQuant with the MLX-VLM CLI using --kv-bits or --kv-quant-scheme turboquant. Reduce memory by up to 76% without compromising quality.

- Tags: how-to-guide
- Published: 2026-04-05

### [Supported Bit Widths for TurboQuant in MLX-VLM: Integer and Half-Integer Values Explained](/Blaizzy/mlx-vlm/turboquant-supported-bit-widths)

Explore TurboQuant's supported bit widths in MLX-VLM discover integer and half-integer options. Learn about the minimum accepted value and how fractional values are handled to optimize your models.

- Tags: deep-dive
- Published: 2026-04-05

### [How TurboQuant Reduces Memory Usage for KV Cache in mlx‑vlm](/Blaizzy/mlx-vlm/turboquant-reduce-kv-cache-memory)

Discover how TurboQuant slashes KV cache memory usage with low-bit integer quantization, packing keys and values into compact codebook indices to save significant memory.

- Tags: performance
- Published: 2026-04-05

### [TurboQuant KV Cache Quantization in MLX-VLM: Low-Bit Compression for Efficient Generation](/Blaizzy/mlx-vlm/turboquant-kv-cache-quantization-mlx-vlm)

Discover TurboQuant KV Cache Quantization in MLX-VLM. This low-bit compression scheme efficiently reduces memory usage during generation, maintaining attention score fidelity.

- Tags: deep-dive
- Published: 2026-04-05

### [How VisionFeatureCache Speeds Up Multi-Turn Conversations in MLX-VLM](/Blaizzy/mlx-vlm/vision-feature-cache-speedup-multiturn)

Discover how VisionFeatureCache accelerates multi-turn conversations in MLX-VLM by caching image features and avoiding repeated vision encoder passes.

- Tags: performance
- Published: 2026-04-05

### [Vision Feature Caching in MLX-VLM: How It Speeds Up Multi-Turn Image Conversations](/Blaizzy/mlx-vlm/vision-feature-caching-mlx-vlm)

Discover Vision Feature Caching in MLX-VLM. This LRU cache stores pre-computed vision embeddings to speed up multi-turn conversations by preventing redundant image processing.

- Tags: internals
- Published: 2026-04-05

### [How to Perform Video Understanding with MLX-VLM: Architecture and API Guide](/Blaizzy/mlx-vlm/video-understanding-mlx-vlm)

Master video understanding with MLX-VLM. Learn the architecture and API guide to process video frames efficiently using Python or CLI. Explore the power of vision-language models.

- Tags: architecture
- Published: 2026-04-05

### [How to Run the MLX-VLM FastAPI Server: A Complete Setup Guide](/Blaizzy/mlx-vlm/run-mlx-vlm-fastapi-server)

Easily run the MLX VLM FastAPI server with our setup guide. Use the mlx_vlm server CLI to preload models, then access OpenAI compatible endpoints at localhost 8080 for seamless integration.

- Tags: how-to-guide
- Published: 2026-04-05

### [How to Use the mlx_vlm.chat_ui Command: Launching Vision-Language Models with Gradio](/Blaizzy/mlx-vlm/mlx-vlm-chat-ui-command-usage)

Launch Vision-Language Models locally with mlx_vlm.chat_ui. Explore this Gradio interface for multimodal text and image chatting using the MLX runtime.

- Tags: how-to-guide
- Published: 2026-04-05

### [Complete Guide to MLX-VLM CLI Commands: Generate, Convert, Chat, and Serve Models](/Blaizzy/mlx-vlm/mlx-vlm-cli-commands)

Master MLX-VLM CLI commands from generate and convert to chat and serve models. Explore this complete guide to leverage the full power of the Blaizzy/mlx-vlm repository.

- Tags: how-to-guide
- Published: 2026-04-05

### [How to Apply Chat Templates in MLX-VLM: Complete Guide](/Blaizzy/mlx-vlm/apply-chat-template-mlx-vlm)

Learn how to apply chat templates in MLX-VLM with this complete guide. Discover how to use apply_chat_template() for seamless prompt construction with multimodal inputs.

- Tags: how-to-guide
- Published: 2026-04-05

### [How to Load a Model and Processor in MLX-VLM: A Complete Guide](/Blaizzy/mlx-vlm/load-model-and-processor-mlx-vlm)

Effortlessly load model and processor in MLX-VLM using mlx_vlm.utils.load. Get your MLX-VLM setup for inference quickly with this simple guide.

- Tags: how-to-guide
- Published: 2026-04-05

### [Load an image (local file, URL, or base64)](/Blaizzy/mlx-vlm/mlx-vlm-generation-pipeline-flow)

Learn how to load images from local files URLs or base64 into MLX VLM. Understand the generation pipeline for efficient image processing and model integration.

- Tags: how-to-guide
- Published: 2026-04-05

### [Core Layers of the MLX-VLM Architecture: A Deep Dive into the Multimodal Pipeline](/Blaizzy/mlx-vlm/mlx-vlm-architecture-core-layers)

Explore the core layers of the MLX-VLM architecture. Understand its three-stage multimodal pipeline featuring VisionModel, LanguageModel, and MultimodalEmbedder for seamless visual and text integration.

- Tags: deep-dive
- Published: 2026-04-05