AI Model, Hardware & Software Comparisons
Head-to-head comparisons to help you pick the right model, hardware, or local AI app. Every comparison includes a category breakdown and a clear verdict.
- Comparisons
- 18
- Model vs model
- 7
- Hardware
- 6
- GPU
- 4
- Software
- 1
Interactive tool
Compare any two machines
Pick two Macs, iPhones or GPUs, including the exact chip configuration, and see the best model each one runs, its estimated speed and how much context fits.
Table 1
Model vs model
Compare open-weight AI model families for local inference with Ollama.
| Comparison | Verdict | Winner | Categories |
|---|---|---|---|
| Qwen vs LlamaQwen vs Llama: Which Model Family Is Better for Local AI? | Qwen is the stronger family for most local setups in 2026. Qwen3.5 4B and 9B fit 8-16 GB Macs, and Qwen3.6 27B fits a 24 GB machine. Llama 4 Scout needs about 80 GB of RAM, so most Llama users still run Llama 3.1 8B or Llama 3.3 70B. Pick Llama for its ecosystem; pick Qwen for current-generation fit. | Qwen wins | 6 categories |
| Qwen vs DeepSeekQwen vs DeepSeek: Versatility vs Visible Reasoning | Qwen is the better daily driver on most Macs, with current-generation models from 2 GB to 96 GB of RAM. DeepSeek R1 distills remain the pick for step-by-step reasoning, and the 7B distill needs just 10 GB. DeepSeek V4 Flash is open weight but wants a 128 GB Mac, so it stays niche locally. | Qwen wins | 5 categories |
| Llama vs MistralLlama vs Mistral: Ecosystem Giant vs Mid-Range Specialist | It depends on your RAM. Llama 3.1 8B is still the safest default on 16 GB Macs and has the biggest ecosystem. Mistral serves 18-26 GB machines better with Nemo 12B and Small 3.1 24B. At the high end, Llama 3.3 70B and Llama 4 face Mistral Medium 3.5, which needs a 96 GB Mac. | Tie | 5 categories |
| DeepSeek vs LlamaDeepSeek vs Llama: Reasoning Power vs All-Round Quality | Llama is the better all-rounder for daily use, with natural chat and the biggest community. DeepSeek R1 distills win when you need step-by-step reasoning for math, logic, or hard debugging. Most users should default to Llama 3.1 8B and switch to the R1 7B distill for hard problems. | Llama wins | 5 categories |
| Gemma vs PhiGemma vs Phi: The Best Small Models for Low RAM | Gemma 4 is the fresher family and covers more tiers. Gemma 4 E4B runs on 8 GB Macs with image input, and the 26B-A4B MoE fits 24 GB machines. Phi-4 Mini stays the lightest quality pick at a 3.2 GB load, and Phi-4 14B holds up for dense reasoning. For most buyers, Gemma 4 wins. | Gemma wins | 5 categories |
| Mistral vs QwenMistral vs Qwen: Focused Lineup vs Full Coverage | Qwen wins on size range, refresh pace, and multilingual breadth. Its current generation has a model for every Mac from 4 GB to 96 GB. Mistral answers with strong mid-range options: Nemo 12B for multilingual work and Small 3.1 24B for coding. For most users, Qwen is the better default. | Qwen wins | 5 categories |
| Phi vs LlamaPhi vs Llama: Tiny Reasoner or Family You Grow With? | Phi-4 Mini wins on 8 GB Macs: a 3.2 GB load with reasoning above its weight class. Llama wins from 16 GB up, with Llama 3.1 8B as the ecosystem default and 70B-class options beyond. Phi tops out at Phi-4 14B, so Llama is the family you can grow with. | Tie | 5 categories |
Table 2
Hardware comparisons
Compare Apple Silicon chips, RAM configurations, and Mac models for running AI locally.
| Comparison | Verdict | Winner | Categories |
|---|---|---|---|
| Apple M4 vs Apple M3M4 vs M3 for Local AI: Is the Upgrade Worth It? | The M4 delivers about 13% faster inference than M3 on an 8B Q4 model and supports up to 32 GB on MacBook Air. For most local AI tasks, M3 is still excellent and the upgrade is not essential. If you are buying new, M4 is the clear pick. If you already have M3, the jump is incremental. | Apple M4 wins | 5 categories |
| Apple M4 Pro vs Apple M4 MaxM4 Pro vs M4 Max for LLMs: When Does Max Make Sense? | M4 Max delivers about 75% faster inference than M4 Pro on the same 8B Q4 model (top Max bin), thanks to 2x memory bandwidth and more GPU cores. It is worth the upgrade only if you regularly run 30B+ models or need maximum speed. For 7B-14B models, M4 Pro is more than sufficient. | Tie | 5 categories |
| Apple M5 Pro vs Apple M5 MaxM5 Pro vs M5 Max for Local LLMs: Which Should You Buy? | The M5 Max wins on raw capability. 2x the memory bandwidth (614 vs 307 GB/s) and 128GB of unified memory let it run 70B models the M5 Pro cannot comfortably handle. But both share the M5 generation's real breakthrough: Neural Accelerators in every GPU core cut prompt processing 3.3-4x versus M4. For 7-35B models, which cover most local AI, the M5 Pro is the smarter buy. Choose the M5 Max only if you run 70B+ models or need maximum bandwidth. | Tie | 5 categories |
| Mac Mini M4 vs Mac Studio M4 MaxMac Mini vs Mac Studio for Local AI: Desktop Showdown | Mac Mini M4 Pro with 48 GB RAM is the best value desktop for local AI. It handles 14B-32B models at good speed. Mac Studio is only worth it if you need 70B+ models or maximum inference speed for production workloads. Most individual users should pick the Mini. | Mac Mini M4 wins | 5 categories |
| 16 GB RAM vs 32 GB RAM16 GB vs 32 GB RAM for Local AI: How Much Memory Do You Actually Need? | 32 GB is the sweet spot for serious local AI use. It comfortably runs 14B models that deliver near-GPT-3.5 quality and leaves room for multitasking. 16 GB works for 7B models but limits you to mid-tier quality. If buying a new Mac, the modest memory upgrade to 32 GB is worthwhile. | 32 GB RAM wins | 5 categories |
| 8 GB RAM vs 16 GB RAM8 GB vs 16 GB RAM for Local AI: Can You Run LLMs on 8 GB? | 8 GB works for small models (Phi-4 Mini 3.8B, Qwen 3B, Llama 3.2 3B) but cannot run the 7-8B models that deliver truly useful quality. 16 GB is the minimum recommended for a good local AI experience. Moving to 16 GB is the single most impactful upgrade for AI. | 16 GB RAM wins | 5 categories |
Table 3
GPU comparisons
Compare NVIDIA GPUs vs Apple Silicon for local LLM inference speed and value.
| Comparison | Verdict | Winner | Categories |
|---|---|---|---|
| NVIDIA RTX 4070 (12 GB) vs Apple M4 (16-32 GB unified)RTX 4070 vs Apple M4: GPU or Apple Silicon for Local AI? | Apple M4 with 32 GB unified memory can run larger models (14B+) because it is not limited to 12 GB VRAM. The RTX 4070 is faster per token on models that fit in its 12 GB VRAM. For maximum model size and simplicity, choose M4. For maximum speed on 7B models, choose RTX 4070. | Tie | 5 categories |
| NVIDIA RTX 5070 (12 GB) vs NVIDIA RTX 4080 (16 GB)RTX 5070 vs RTX 4080 for LLMs: New Architecture or More VRAM? | The RTX 4080 with 16 GB VRAM runs larger models and longer contexts than the RTX 5070 with 12 GB. Bandwidth is close between them (717 vs 672 GB/s, about 7%), so token speed is close too. For LLM inference specifically, VRAM is the deciding factor. Buy the 4080 used if you can find one at a good price. | NVIDIA RTX 4080 (16 GB) wins | 5 categories |
| NVIDIA GPU (Dedicated VRAM) vs Apple Silicon (Unified Memory)GPU vs Apple Silicon: Which Architecture Is Better for Local AI? | Apple Silicon wins on maximum model size per dollar because unified memory does not split into separate pools. NVIDIA GPUs win on raw speed for models that fit in VRAM. For most individual users running 7B-14B models, Apple Silicon is simpler and more cost-effective. For maximum speed on 7B models or professional serving, NVIDIA GPUs are faster. | Tie | 5 categories |
| NVIDIA RTX 5070 Ti (16 GB) vs NVIDIA RTX 5080 (16 GB)RTX 5070 Ti vs RTX 5080 for LLMs: Same 16GB, Different Value | The RTX 5070 Ti is the better value for local AI. Both cards run the same 14B models on 16GB, and the 5080 is only about 6% faster (an estimated 132 vs 125 tok/s on 8B) while costing more. Unless you want the absolute fastest 16GB card, the 5070 Ti delivers a similar share of the speed for less. Choose the 5080 only if maximum throughput matters more than cost. | NVIDIA RTX 5070 Ti (16 GB) wins | 5 categories |
Table 4
Software vs software
Compare local AI apps by workflow, extensibility, and ease of use.
| Comparison | Verdict | Winner | Categories |
|---|---|---|---|
| Draw Things vs ComfyUIDraw Things vs ComfyUI: Which Local AI App Fits Your Workflow? | Choose Draw Things for the simplest native app with integrated editing and on-device LoRA training. Choose ComfyUI for custom nodes, detailed pipeline control, workflow JSON and a local API. Overall it is a tie: no official matched-hardware comparison establishes a universal speed, RAM or image-quality winner. | Tie | 4 categories |