LLM Inference on Apple Silicon — M4 Performance Guide (2026)
Apple Silicon is uniquely good for LLM inference because of unified memory — your GPU can use ALL system RAM. A 32GB Mac has 32GB of effective VRAM. No other consumer platform offers this. If you’ve been wondering how much VRAM you need for AI, the answer on Mac is simple: however much RAM your machine has.
Update (April 24, 2026): DeepSeek V4 Flash (13B active params) may become a strong Apple Silicon option when GGUF quantizations are available. See how to run V4 locally.
See our Best AI Models for Mac for model recommendations. This guide focuses on inference performance across every generation of Apple Silicon.
Why unified memory changes everything
On a traditional PC, your GPU has its own dedicated VRAM — 8GB, 12GB, 16GB, or 24GB on consumer cards. The CPU has separate system RAM. Moving data between them is slow because it crosses the PCIe bus. This creates a hard ceiling: if your model doesn’t fit in VRAM, you’re stuck with partial offloading and terrible performance.
Apple Silicon eliminates this entirely. The CPU, GPU, and Neural Engine all share a single pool of memory. There’s no copying, no bus bottleneck, and no artificial split. A Mac Mini with 32GB of RAM gives the GPU full access to all 32GB. That’s more effective VRAM than an RTX 4090 (24GB) at a fraction of the price.
This architectural advantage is why Apple Silicon punches well above its weight for LLM inference, even though NVIDIA GPUs have higher raw compute throughput. For models that are memory-bandwidth-bound (which most LLM inference is), unified memory is a genuine game-changer.
M1 through M4: generation-by-generation comparison
Each generation of Apple Silicon has improved memory bandwidth and GPU core count, both of which directly impact LLM inference speed.
| Chip | Max RAM | Memory bandwidth | GPU cores | Relative LLM speed |
|---|---|---|---|---|
| M1 | 16GB | 68 GB/s | 8 | 1.0x (baseline) |
| M1 Pro | 32GB | 200 GB/s | 16 | ~2.5x |
| M1 Max | 64GB | 400 GB/s | 32 | ~4.5x |
| M1 Ultra | 128GB | 800 GB/s | 64 | ~8x |
| M2 | 24GB | 100 GB/s | 10 | ~1.4x |
| M2 Pro | 32GB | 200 GB/s | 19 | ~2.7x |
| M2 Max | 96GB | 400 GB/s | 38 | ~5x |
| M2 Ultra | 192GB | 800 GB/s | 76 | ~8.5x |
| M3 | 24GB | 100 GB/s | 10 | ~1.5x |
| M3 Pro | 36GB | 150 GB/s | 18 | ~2.2x |
| M3 Max | 128GB | 400 GB/s | 40 | ~5.2x |
| M4 | 32GB | 120 GB/s | 10 | ~1.7x |
| M4 Pro | 48GB | 273 GB/s | 20 | ~3.5x |
| M4 Max | 128GB | 546 GB/s | 40 | ~6.5x |
| M4 Ultra | 192GB | 800 GB/s | 80 | ~9x |
Memory bandwidth is the single most important spec for LLM inference. The M4 Ultra’s 800 GB/s puts it in the same ballpark as data center GPUs, while the base M4 at 120 GB/s is still respectable for smaller models.
Benchmarks (tokens/second, Ollama, Q4 quantization)
| Model | M4 16GB | M4 32GB | M4 Pro 48GB | M4 Ultra 192GB |
|---|---|---|---|---|
| Qwen 3.5 9B | 35 | 38 | 42 | 45 |
| Codestral 22B | 12 | 25 | 30 | 35 |
| Qwen 3.5 27B | ❌ | 22 | 28 | 32 |
| Devstral Small 24B | ❌ | 20 | 26 | 30 |
| Mistral Large 123B | ❌ | ❌ | ❌ | 6 |
| DeepSeek V3 671B | ❌ | ❌ | ❌ | 3 |
❌ = doesn’t fit in memory
For context, 20+ tokens/second feels interactive and responsive. 10-20 is usable but you’ll notice the delay. Below 10 is only practical for batch processing or when you need the absolute best quality from a massive model.
Metal acceleration and the GPU pipeline
Apple’s Metal framework is what makes GPU-accelerated inference possible on Mac. Both Ollama and MLX use Metal under the hood to run matrix multiplications on Apple’s GPU cores. Metal 3 (M3/M4) added hardware-accelerated ray tracing and mesh shading, but more importantly for AI, it improved the compute shader pipeline that LLM inference relies on.
When you run a model through Ollama on Mac, here’s what happens:
- The model weights are loaded into unified memory
- Metal compute shaders execute matrix multiplications on the GPU
- Results are read directly from the same memory pool — no copy needed
- The next token is generated and the process repeats
This tight loop, with zero memory copies, is why Apple Silicon achieves surprisingly high tokens/second despite having lower raw TFLOPS than NVIDIA GPUs.
MLX framework: Apple’s native ML library
MLX is Apple’s open-source machine learning framework, designed specifically for Apple Silicon. It’s built by Apple’s ML research team and takes full advantage of unified memory in ways that even Metal-based inference engines can’t always match.
Key advantages of MLX:
- Lazy evaluation — computations are only executed when results are needed, reducing unnecessary work
- Unified memory by design — arrays live in shared memory accessible by CPU and GPU without copies
- Dynamic graph — similar to PyTorch, making it familiar for ML developers
- 10-20% faster than Ollama on certain models, particularly larger ones where memory efficiency matters most
The MLX community on Hugging Face maintains a growing library of pre-converted models. You’ll find most popular models in GGUF and MLX formats. To get started:
pip install mlx-lm
mlx_lm.generate --model mlx-community/Qwen2.5-Coder-32B-Instruct-4bit --prompt "Write a Python function"The downside is ecosystem support. LM Studio has added MLX backend support, which helps, but most AI coding tools (Aider, Continue.dev, OpenCode) connect through Ollama’s OpenAI-compatible API. If you’re using these tools, stick with Ollama.
Ollama vs MLX
| Ollama | MLX | |
|---|---|---|
| Ease of use | ollama run model | Requires Python |
| Speed | Good | 10-20% faster on some models |
| Model library | Huge | Limited (mlx-community on HF) |
| Tool support | Aider, Continue.dev, OpenCode | Limited |
| Quantization | GGUF (Q4, Q5, Q8) | MLX native + GGUF |
| API compatibility | OpenAI-compatible | Custom Python API |
Recommendation: Use Ollama unless you need maximum speed on a specific model. The ecosystem support is much better. If you’re running a dedicated inference server and want every last token/second, MLX is worth the setup effort.
Best models per chip tier
Choosing the right model for your hardware is critical. Running a model that barely fits in memory will be painfully slow due to swap. For models larger than your Mac’s RAM can handle, cloud GPU providers are the next step up. Here’s what works well at each tier, focusing on models that fit comfortably with room for context:
8-16GB RAM (M1, M2, M3, M4 base): Stick with models under 16GB VRAM. Qwen 3.5 9B Q4 (~5GB), Llama 3.1 8B Q4 (~4.5GB), and Phi-3 Mini are your best options. These leave enough RAM for a decent context window and your OS.
24-32GB RAM (M2/M3/M4 with upgraded RAM): This is the sweet spot. Qwen 3.5 27B Q4 (~15GB), Codestral 22B Q4 (~12GB), and DeepSeek Coder V2 Lite fit comfortably. You get near-frontier quality for coding tasks.
48-64GB RAM (M4 Pro, M3 Max): Run 32B-70B parameter models. Llama 3.1 70B Q4 (~38GB), Qwen 2.5 72B Q4 (~40GB). These compete with cloud APIs for most tasks.
128-192GB RAM (M4 Max, M4 Ultra): The only consumer hardware that can run 100B+ models locally. Mistral Large 123B, Llama 3.1 405B Q2 (with quality trade-offs), and even DeepSeek V3 671B at very low quantization.
Why Apple Silicon is different
Unified memory: CPU and GPU share the same RAM. No PCIe bottleneck for data transfer. This is why a 32GB Mac outperforms a 16GB GPU for large models — the GPU can access all 32GB.
Memory bandwidth: M4 Ultra has 800 GB/s bandwidth. Not as fast as an H100 (3.35 TB/s) but far better than consumer GPUs (1 TB/s for RTX 4090).
Power efficiency: A Mac Mini running a 27B model uses ~30W. An RTX 4090 uses ~300W. For 24/7 inference, the electricity savings are significant. Over a year of continuous operation, that’s roughly $200 in electricity for the Mac versus $2,000 for the NVIDIA setup.
Silence: Mac Minis and MacBook Pros run near-silent under inference loads. If you’re running a model as a local coding assistant, you won’t hear it. A PC with an RTX 4090 sounds like a jet engine under sustained load.
Best Mac for each use case
| Use case | Mac | Model | Monthly cost |
|---|---|---|---|
| Personal coding assistant | Mac Mini M4 32GB ($1,150) | Qwen 3.5 27B | $0 |
| Team server (2-3 devs) | Mac Mini M4 Pro 48GB ($1,800) | Qwen 2.5 Coder 32B | $0 |
| Frontier-class local | Mac Studio Ultra 192GB (~$6,000) | DeepSeek V3 | $0 |
Compare to API costs: a Mac Mini M4 32GB replaces ~$50-100/month in API fees. Pays for itself in a year. See our inference cost calculator.
Practical tips for maximizing performance
- Close memory-hungry apps before running large models. Safari with many tabs, Docker, and Electron apps all compete for unified memory.
- Use Q4_K_M quantization as the default — it’s the best balance of quality and speed. Q5_K_M if you have RAM to spare, Q3_K_M if you’re tight.
- Set context length explicitly in Ollama with
num_ctx. The default 2048 is often too small, but 32768 uses significant memory. Match it to your actual needs. - Monitor with
asitop— this free tool shows real-time GPU utilization, memory bandwidth usage, and power consumption on Apple Silicon.
FAQ
Can I run LLMs on M1?
Yes. The base M1 with 16GB RAM can run 7-9B parameter models at Q4 quantization comfortably, getting around 15-20 tokens/second. That’s fast enough for interactive use. You won’t be running 27B+ models, but for a coding assistant using Qwen 3.5 9B or Llama 3.1 8B, the M1 is perfectly capable. The M1 Pro and Max with more RAM open up larger models.
Is Apple Silicon faster than NVIDIA for LLMs?
Not in raw throughput. An RTX 4090 generates tokens faster than any current Apple Silicon chip for models that fit in its 24GB VRAM. But Apple Silicon wins on effective VRAM (unified memory means all your RAM is available), power efficiency (10x less power draw), cost per GB of VRAM, and silence. For models larger than 24GB — which includes most 27B+ models — a 32GB Mac often beats an RTX 4090 because the model actually fits without offloading.
How much RAM do I need on Mac?
For casual use with 7-9B models: 16GB works. For serious local AI development with 22-27B models: 32GB is the sweet spot and our top recommendation. For running 70B+ models or serving multiple users: 48-64GB. For frontier models (100B+): 128GB or 192GB. When in doubt, get 32GB — it’s the best value for AI workloads on Mac today.
Related: Best AI Models for Mac · Ollama Complete Guide · LM Studio Complete Guide · How Much VRAM for AI? · Best AI Models Under 16GB VRAM · GPU Memory Planning · LLM Inference Cost Calculator · Cpu Vs Gpu Llm Inference · Run Multiple Models One Gpu