Local LLM Comparisons for Mac
According to LLMCheck, the questions Mac users ask most are which open model to run, which Apple Silicon chip to buy, and whether to run AI locally or in the cloud. These pages answer each head-to-head with figures from the LLMCheck index, every speed labelled measured or estimated. For any two to four models, use the compare tool.
Updated 24 October 2026 · 21 comparisons · compare tool · full leaderboard
Compare 2–4 models side by side
Pick any models from the index and see their LLMCheck Scores, minimum RAM, licences and speeds with provenance, with a shareable link.
Apple Silicon for AI: Every Chip Compared
Memory bandwidth, GPU cores and index speeds for every Apple Silicon generation, M1 to M6, and which chip gives the best value for running LLMs.
Local LLM vs Cloud AI: Privacy, Speed & Cost Compared
Should you run AI on your Mac or use ChatGPT or Claude? Privacy, latency, cost and capability, with the scenarios where each approach wins.
Head-to-head
Every comparison on LLMCheck, grouped by question. Each one names the chip and the provenance of every speed it quotes.
Models
- Qwen 3.6 vs Gemma 4 on a Mac — speed, coding and RAM for the two families that lead the 24–32 GB tier
- Gemma 4 vs Qwen 3.5 — the April 2026 matchup, by Mac and by task
- DeepSeek V4 Pro vs Kimi K2.6 for coding — two frontier coders, and what fits a Mac instead
- GLM 5.2 vs Qwen3.8-Max vs Kimi K3 — the open-frontier three-way, by licence and size
- Qwen3.8-Max vs Kimi K3 vs Inkling — frontier flagships and the siblings that run locally
- GLM 5.2 vs DeepSeek V4 — MIT-licensed frontier models compared
- Llama 3.3 vs Hermes 4 vs Apertus 1.5 — the 70B-class models that actually exist
- Qwen 4 vs “Qwen 4.1” — what exists, what doesn’t, and what to run now
- DeepSeek V3.2 vs GPT-5 — how close open weights came to the frontier
- Mixture-of-experts vs dense models — why MoE models run faster on a Mac
Macs and chips
- M5 Ultra vs M3 Ultra vs M5 Max — decode, prompt speed and what fits in 256 and 512 GB
- M5 Max vs M4 Max — 614 vs 546 GB/s, and whether the upgrade pays
- M5 Pro vs M5 Max — 307 vs 614 GB/s for local LLMs
- M4 vs M5 — M5-generation chips against their M4 equivalents
- M4 Max vs M3 Max — the previous Max upgrade, by bandwidth
- Every Apple Silicon chip for AI — M1 to M6, Pro, Max and Ultra in one table
Approach and apps
- Local LLM vs cloud AI — privacy, speed and cost, with the cases where each wins
- DeepSeek R1 vs Claude — local vs cloud for developers
- LM Studio vs Ollama — the two most-used Mac apps for local models
- RAG vs fine-tuning on a Mac — when to use each for local AI
- The index’s estimates vs measured runs — how accurate Mac speed estimates are
Frequently Asked Questions
Should I use local AI or cloud AI on my Mac?
According to LLMCheck, local AI is better for privacy, offline access and zero ongoing cost; cloud AI (ChatGPT, Claude) is better for frontier-class reasoning. Most power users combine both: local for private data and quick tasks, cloud for the hardest reasoning. On a Mac with 32 GB or less, the LLMCheck index's best local pick is Qwen 3.6-35B-A3B.
Which Apple Silicon chip is best for running local AI?
The M5 Ultra (1,228 GB/s, up to 512 GB) is the fastest and holds the largest models, such as MiMo-V2.6-Flash (needs 192 GB or more; 61.7 tok/s community-measured on an M5 Ultra). The M5 Max (614 GB/s, up to 128 GB) is the strongest laptop chip: Qwen 3.6-35B-A3B, the LLMCheck index's #1 and its best pick at 32 GB or less, runs at 118 tok/s there, a published figure. Memory bandwidth sets generation speed.
How do I choose between different AI models for Mac?
Start from your Mac's memory: the model must fit with room to spare. Then weigh capability, speed on your chip and licence. The LLMCheck Score combines those into one 0–100 ranking, the leaderboard filters by RAM, and the compare tool puts two to four models side by side with their speeds and provenance.
Full Model Rankings
Every model in the index on LLMCheck’s sortable leaderboard, filterable by your Mac’s memory.
View Leaderboard →