@prefix schema: <https://schema.org/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix dbpedia: <http://dbpedia.org/resource/> .
@prefix : <https://docs.google.com/document/d/1D0wqfiCRhh6AMyk9x8fKYTIzJvZYmY4fNoW6qdPfIo4/edit?tab=t.0#> .

# --- Document Metadata ---
:it a schema:DigitalDocument ;
    schema:name "The Best Local AI Models by Hardware — April 2026" ;
    schema:author [
        a schema:Organization ;
        schema:name "Levangie Labs" ;
        schema:url <https://levangielabs.com> 
    ] ;
    schema:datePublished "2026-04-04"^^xsd:date ;
    schema:about :faq, :glossary, :howToInstall, :hardwareComparison ;
    schema:mentions dbpedia:Large_language_model, dbpedia:Nvidia, dbpedia:Apple_Inc, dbpedia:Quantization .

# --- Expanded FAQ Section (15+ Questions) ---
:faq a schema:FAQPage ;
    schema:name "Frequently Asked Questions about Local LLMs (April 2026)" ;
    schema:mainEntity :q1, :q2, :q3, :q4, :q5, :q6, :q7, :q8, :q9, :q10, :q11, :q12, :q13, :q14, :q15 .

:q1 a schema:Question ;
    schema:name "What is the primary factor determining local LLM speed?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "Memory bandwidth. Since models read weights from memory for every token, faster bandwidth directly increases tokens per second[cite: 1]." ] .

:q2 a schema:Question ;
    schema:name "What makes Apple Silicon unique for local AI?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "Unified memory allows the entire RAM pool to be shared between the CPU and GPU, enabling large models to run on consumer hardware that would typically require expensive enterprise GPUs[cite: 1]." ] .

:q3 a schema:Question ;
    schema:name "Can a 1 trillion parameter model run locally?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "Yes, Kimi K2.5 (1T params) can run on a Mac Studio M3 Ultra with 512GB RAM using 1.8-bit quantization[cite: 1]." ] .

:q4 a schema:Question ;
    schema:name "What is the 'Sweet Spot' hardware for local AI in 2026?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "The Mac Mini M4 Pro with 48GB of RAM is considered the best-value setup for its price and ability to run 32B models efficiently[cite: 1]." ] .

:q5 a schema:Question ;
    schema:name "What is the fastest consumer GPU for local inference?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "The NVIDIA RTX 5090, with approximately 1.8 TB/s memory bandwidth, is the top consumer choice[cite: 1]." ] .

:q6 a schema:Question ;
    schema:name "How fast is AI on the iPhone 17 Pro?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "The 1-bit Bonsai 8B model achieves over 40 tokens per second on the iPhone 17 Pro[cite: 1]." ] .

:q7 a schema:Question ;
    schema:name "What is the benefit of Mixture of Experts (MoE) models?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "MoE models only activate a fraction of their total parameters per token, allowing massive models like Qwen3 235B to run at speeds comparable to much smaller models[cite: 1]." ] .

:q8 a schema:Question ;
    schema:name "How much did Ollama's performance improve on Mac recently?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "With the addition of native MLX support in April 2026, Ollama users see a 1.5-2x speedup on Apple Silicon[cite: 1]." ] .

:q9 a schema:Question ;
    schema:name "Can smart rings run AI models?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "Newer rings using the Ambiq Apollo330B Plus chip can run specialized 200 million parameter models for health and gesture tasks[cite: 1]." ] .

:q10 a schema:Question ;
    schema:name "What is the best local coding model?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "Qwen 2.5 Coder 32B is the top recommendation for local coding, offering high quality on mid-tier hardware like the Mac Mini M4 Pro[cite: 1]." ] .

:q11 a schema:Question ;
    schema:name "Does the NVIDIA DGX Spark fit in a home office?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "Yes, the DGX Spark is NVIDIA’s consumer-tier 'personal AI supercomputer' priced at roughly $3,000[cite: 1]." ] .

:q12 a schema:Question ;
    schema:name "What is 'SSD streaming' for AI models?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "A breakthrough in tools like anemll-flash-llama.cpp allows streaming MoE experts directly from an SSD, enabling huge models to run on machines with less RAM[cite: 1]." ] .

:q13 a schema:Question ;
    schema:name "How do Liquid AI models differ from standard LLMs?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "Liquid AI's LFM2 models use Gated Delta Networks instead of Transformers, providing significantly faster inference speeds at the same parameter count[cite: 1]." ] .

:q14 a schema:Question ;
    schema:name "Is a CPU-only laptop viable for AI?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "It is slow (3-6 tok/s), but small models like Phi-4 Mini 3.8B are usable for non-interactive or privacy-sensitive tasks[cite: 1]." ] .

:q15 a schema:Question ;
    schema:name "Will Apple Watch ever run local LLMs?" ;
    schema:acceptedAnswer [ a schema:Answer ; schema:text "Not currently, as the 1GB RAM limit is too low, but sub-1B models may become viable by 2027-2028 as quantization improves[cite: 1]." ] .

# --- Glossary Section ---
:glossary a schema:DefinedTermSet ;
    schema:name "Glossary of AI and Hardware Terms" ;
    schema:hasDefinedTerm :termQuantization, :termMoE, :termUnifiedMemory, :termBandwidth .

:termQuantization a schema:DefinedTerm ;
    schema:name "Quantization" ;
    schema:description "Reducing the precision of model weights to shrink memory footprint and increase speed[cite: 1]." ;
    schema:sameAs dbpedia:Quantization .

:termMoE a schema:DefinedTerm ;
    schema:name "Mixture of Experts (MoE)" ;
    schema:description "A model architecture that only uses a subset of parameters for each calculation[cite: 1]." .

:termUnifiedMemory a schema:DefinedTerm ;
    schema:name "Unified Memory" ;
    schema:description "Hardware architecture where RAM is shared between CPU and GPU[cite: 1]." .

:termBandwidth a schema:DefinedTerm ;
    schema:name "Memory Bandwidth" ;
    schema:description "The speed at which data is read from or written to memory[cite: 1]." ;
    schema:sameAs dbpedia:Memory_bandwidth .

# --- How-To Section ---
:howToInstall a schema:HowTo ;
    schema:name "How to Optimize Local AI Performance" ;
    schema:description "Key steps for getting the most out of local LLMs[cite: 1]." ;
    schema:step :step1, :step2, :step3 .

:step1 a schema:HowToStep ;
    schema:name "Select Quantized Models" ;
    schema:text "Choose Q4_K_M or 1.8-bit quants to balance model intelligence with hardware memory limits[cite: 1]." ;
    schema:position 1 .

:step2 a schema:HowToStep ;
    schema:name "Use Hardware-Specific Tools" ;
    schema:text "Use MLX-LM for Mac or TensorRT-LLM for NVIDIA to maximize token generation speed[cite: 1]." ;
    schema:position 2 .

:step3 a schema:HowToStep ;
    schema:name "Enable GPU Offloading" ;
    schema:text "Ensure your inference engine (like Ollama) is configured to offload as many layers as possible to the GPU/VRAM[cite: 1]." ;
    schema:position 3 .

# --- Hardware Tier Data (Extracted from Tables) ---
:hardwareComparison a schema:Dataset ;
    schema:name "Model Performance by Hardware Tier" ;
    schema:hasPart :tier1, :tier2, :tier3, :tier4 .

:tier1 a schema:ItemList ;
    schema:name "Enterprise NVIDIA" ;
    schema:itemListElement [ a schema:ListItem ; schema:name "Llama 3.1 405B Q4" ; schema:description "8-15 tok/s on DGX Station [cite: 1]" ] .

:tier4 a schema:ItemList ;
    schema:name "Consumer Sweet Spot" ;
    schema:itemListElement [ a schema:ListItem ; schema:name "Qwen 3 32B Q4" ; schema:description "12-22 tok/s on Mac Mini M4 Pro [cite: 1]" ] .