Hardware · Apple M-Series · Reviewed June 2026
Apple Silicon for Local LLMs: What Unified Memory Changes
Apple Silicon with 64GB of unified memory is not a faster NVIDIA card — it is a different architecture. The CPU and GPU share one large memory pool, which means a Mac Studio M3 Ultra can load a 70B model that no consumer NVIDIA GPU can hold in VRAM. The tradeoff is bandwidth: at 400 GB/s, it generates tokens more slowly than a discrete NVIDIA GPU for the same model size. This guide covers what that means in practice.
Editorial review
Apple Silicon memory bandwidth and usable allocation limits vary by chip generation and macOS version. The ~75% usable allocation limit can be changed via sysctl on macOS. Verify specifications against current Apple hardware before purchasing.
Quick verdict
Apple Silicon 64GB is the only consumer hardware that can run a 70B model locally in memory. That is its headline capability. A 70B model at Q4_K_M quantization needs approximately 40–42 GB — which fits within the roughly 48 GB that macOS makes accessible to AI processes on a 64 GB system.
What you give up is token generation speed. At 400 GB/s of memory bandwidth, Apple Silicon generates tokens more slowly than an RTX 4090 (1008 GB/s) for the same model at the same quantization level. A 7B model that runs at 40+ tokens per second on a 4090 runs at 15–25 tokens per second on Apple Silicon, depending on the specific M-series chip.
The trade is clear: if you need to run the largest models locally and speed is secondary, Apple Silicon 64GB is the right architecture. If you need the fastest possible inference on models up to 32B, a discrete NVIDIA GPU at the 24GB tier runs them faster.
What this hardware can run
The 64 GB pool (~48 GB accessible for AI) changes the model ceiling significantly. FP16 becomes usable for 13B and 14B models, and 70B at Q4 fits — both impossible on any consumer NVIDIA card.
| Model size | Best quantization | Memory used | Verdict | Notes |
|---|---|---|---|---|
| 1B–4B | FP16 | 2–8 GB | Comfortable | Full precision on any small model. Very fast generation. |
| 7B–8B | FP16 | ~14–16 GB | Comfortable | Full precision. Slower tokens/sec than NVIDIA at equivalent VRAM due to lower bandwidth. |
| 13B | FP16 | ~26 GB | Comfortable | 13B at FP16 — impossible on any consumer NVIDIA GPU. A genuine architectural advantage. |
| 14B | FP16 | ~28 GB | Comfortable | Qwen 2.5 14B, Phi-4 at full precision. ~28 GB fits within the ~48 GB usable pool. |
| 30B–32B | Q4 or Q8 | ~18–34 GB | Comfortable | Fits well. Q8 on 30B (~34 GB) also fits — not possible on 24 GB NVIDIA. |
| 70B | Q4_K_M | ~40–42 GB | Fits (near limit) | The headline unlock. No consumer NVIDIA GPU can run 70B in VRAM. Expect 3–8 t/s. |
| 70B | Q8 | ~74 GB | Does not fit | Q8 on 70B exceeds the ~48 GB accessible limit. Requires multi-GPU or cloud. |
Note on the ~48 GB limit: macOS dynamically limits how much of the unified memory pool AI processes can use. On a 64 GB system this is typically around 48 GB. You can raise this limit by running sudo sysctl iogpu.wired_limit_mb=57344 (57 GB) in Terminal, though this leaves less headroom for the OS and other applications.
Best model sizes for this hardware
- 14B at FP16: The most compelling daily tier. Full precision on a 14B model — impossible on 12GB or 24GB NVIDIA cards — at around 10–20 tokens per second. Qwen 2.5 14B and Phi-4 at FP16 are strong choices. Excellent quality, no quantization degradation.
- 30B–32B at Q4 or Q8: A 32B model at Q8 (~34 GB) fits comfortably — another tier that NVIDIA 24GB cards cannot reach. Qwen 2.5 32B and Qwen 3 32B are solid options for complex reasoning and deep instruction following.
- 70B at Q4: The unique Apple Silicon use case. Llama 3.1 70B at Q4_K_M runs locally at 3–8 tokens per second. Slow for interactive chat but usable for batch tasks, summarisation, and infrequent high-quality inference without cloud cost.
Recommended models
- Qwen 2.5 14B FP16: Pull with
ollama pull qwen2.5:14b-fp16. Full-precision 14B at ~28 GB. The daily driver recommendation for this hardware class. - Qwen 3 32B Q4_K_M: Pull with
ollama pull qwen3:32b. Strong reasoning and instruction following at 32B depth. ~20 GB at Q4. - Qwen 2.5 32B Q8: Pull with
ollama pull qwen2.5:32b-q8_0. Near-FP16 quality on a 32B model. Fits within ~48 GB. Best quality for complex tasks. - Llama 3.1 70B Q4_K_M: Pull with
ollama pull llama3.1:70b. The hardware's flagship use case. Expect 3–8 t/s depending on the M chip generation. Use for occasional high-quality inference, not fast interactive chat.
Recommended runtimes
- Ollama — Metal acceleration is automatic on Apple Silicon. The easiest path to running any GGUF model on macOS. Use the standard pull and run commands — no configuration needed for Metal.
- LM Studio — Desktop GUI with full Metal support and a built-in model browser. Good for comparing models without a terminal. Handles quantization selection and context window settings visually.
- Open WebUI — Browser-based chat interface over Ollama. Run via Docker Desktop for Mac or directly via pip on macOS.
- llama.cpp (native Metal build): The underlying inference engine. Build from source with
CMAKE_ARGS="-DGGML_METAL=on"for direct control over quantization, context size, and Metal GPU layers. Best for advanced tuning.
Not supported: vLLM, TGI, and other CUDA-dependent runtimes will not run on Apple Silicon. If you need those runtimes for team serving, a Linux machine with an NVIDIA GPU is required.
Best local AI workflows for this hardware
- High-quality single-session inference: 70B or 32B models for tasks where quality matters more than speed — document analysis, structured output, complex reasoning. The slow generation speed is acceptable for non-interactive batch work.
- Developer workspace: Running a fast 14B FP16 model alongside other developer tools. The unified memory architecture means the model and your IDE, browser, and other processes all draw from the same pool without the hard VRAM ceiling of discrete GPUs.
- Multi-model setups: With 64 GB total, you can run one loaded model while keeping context for a second. Useful for workflows that switch between a coding model and a reasoning model.
- Local RAG over large document sets: Large context windows at 30B+ depth can handle substantial document corpora in a single pass.
What this hardware cannot do well
- Fast token generation: At 400 GB/s bandwidth, Apple Silicon generates fewer tokens per second than discrete NVIDIA GPUs for the same model size. A 7B model at Q4 runs at 15–25 t/s on Apple Silicon vs 35–50 t/s on an RTX 4090. If generation speed is your priority, NVIDIA wins at every comparable model size.
- CUDA runtimes: vLLM, TGI, and other CUDA-dependent tools do not run on Apple Silicon. This limits multi-user serving options compared to Linux + NVIDIA setups.
- 70B at Q8: Q8 on a 70B model requires ~74 GB, which exceeds the ~48 GB accessible limit. 70B at Q4 is the practical ceiling.
- Predictable memory allocation: The ~75% usable allocation limit is a macOS system policy, not a fixed hardware ceiling. It can change across macOS versions and may behave differently across M chip generations.
Apple Silicon vs NVIDIA: the honest comparison
| Metric | Apple Silicon 64GB | RTX 4090 24GB |
|---|---|---|
| Max model (in memory) | 70B Q4 | ~32B Q4 |
| 7B generation speed | ~15–25 t/s | ~40–80 t/s |
| 14B FP16 | Yes (~28 GB) | No (exceeds 24 GB) |
| 30B Q8 | Yes (~34 GB) | No (exceeds 24 GB) |
| CUDA runtimes | No | Yes (vLLM, TGI) |
| Ecosystem | macOS only | Windows / Linux |
Upgrade path
- Mac Studio / Mac Pro with 128GB or 192GB unified memory: The natural upgrade within the Apple Silicon ecosystem. 128GB opens 70B at Q8 (~74 GB). 192GB can hold multiple large models simultaneously.
- Switch to Linux + NVIDIA for speed: If token generation speed is the bottleneck and you can live with a 24GB model ceiling, an RTX 4090 on Linux delivers roughly 2.5× the tokens per second for 7B–14B models.
Cloud fallback
For 70B at Q8 or larger models, cloud inference avoids the memory constraint entirely.
- RunPod: A100 80GB and H100 instances for 70B at Q8 or FP16.
- Lambda: ML-focused cloud with A100 and H100 instances.
Related hardware
FAQ
Can Apple Silicon run 70B models locally?
Yes — with 64GB of unified memory, a 70B model at Q4_K_M quantization (~40–42 GB) fits within the approximately 48 GB of memory that is practically accessible for AI workloads. This is the single most important advantage of Apple Silicon 64GB over any consumer NVIDIA GPU, which tops out at 24 GB VRAM. Generation speed at 70B is slow — typically 3–8 tokens per second — but the model runs locally without cloud inference.
Is Apple Silicon VRAM the same as NVIDIA VRAM?
No. Apple Silicon uses a unified memory architecture where the CPU and GPU share one physical memory pool. There is no separate VRAM chip. For local AI, this means the full 64 GB is accessible in principle, though the system typically limits AI processes to around 75 percent (~48 GB) unless configured via sysctl. The tradeoff is bandwidth: at 400 GB/s, Apple Silicon delivers fewer tokens per second per GB than a discrete NVIDIA GPU at equivalent model sizes.
How does Apple Silicon compare to the RTX 4090 for local AI?
For models up to 32B: the RTX 4090 is faster (1008 GB/s vs 400 GB/s) and generates more tokens per second. For 70B models: Apple Silicon 64GB can run 70B at Q4 locally — the RTX 4090 cannot. If fast inference on 7B–32B models is your priority, the RTX 4090 wins. If running 70B models locally without cloud inference is your priority, Apple Silicon 64GB is the only consumer option that can do it.
Can I use CUDA on Apple Silicon?
No. Apple Silicon uses Metal for GPU compute, not NVIDIA CUDA. Runtimes like vLLM require CUDA and will not run on Apple Silicon. Ollama, LM Studio, and llama.cpp all support Metal acceleration on macOS and are the recommended runtimes for Apple Silicon local AI.
Why is token generation slower on Apple Silicon than NVIDIA?
LLM inference (the token generation phase) is memory-bandwidth-bound. Apple Silicon M-series at the Max tier offers around 400 GB/s of memory bandwidth. An RTX 4090 offers 1008 GB/s. More bandwidth means the model weights can be streamed through the compute units faster, producing more tokens per second. Apple Silicon compensates with larger accessible memory — it can fit bigger models — but it cannot match NVIDIA at equivalent generation speed for the same model size.
What Ollama command should I use first on Apple Silicon 64GB?
With 64GB of unified memory, you can start larger than on 12GB NVIDIA cards. Try `ollama pull qwen2.5:32b` for a 32B model at Q4, or `ollama pull llama3.1:70b` for a 70B model that no single NVIDIA consumer GPU can run. For a fast daily driver, `ollama pull qwen3:14b-fp16` runs a 14B model at full FP16 precision — impossible on 12GB or 24GB NVIDIA cards.
Disclosure
OpenSourcesAI may earn a commission or referral fee from links to hardware retailers, cloud GPU providers, or partner tools on this page. Editorial assessments are produced independently. Hardware specs and memory allocation limits are sourced from Apple documentation and community testing. Actual accessible memory and generation speeds vary by macOS version, M chip generation, and workload. Verify before making purchasing decisions.
Check which models fit your Apple Silicon
Use the compatibility checker with your unified memory amount to see which models and quantization levels are recommended for your specific Mac.
For builders
Building tools for Apple Silicon local AI?
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