Apple MacBook Pro 14-inch (M3 Pro 36GB)

Scored against every model in our directory at Q4_K_M, using the ~24.1GB of GPU-usable memory on this machine and its 150 GB/s of memory bandwidth. Of 30 models, 22 run comfortably, 1 is a tight fit, and 7 will not load. The largest model that fits well here is Cohere Command R 35B.

How these are estimated: tokens/sec ≈ memory bandwidth × 0.65 ÷ active weights in GB. These are arithmetic estimates from published specifications, not lab measurements — BFWR does not run a hardware test lab. Real throughput varies with runner, quantization, context length and thermals.

Diffusion models size up differently from language models: the text encoder is often as large as the image model itself, and memory scales with resolution. Measured at 1024×1024, one image at a time, against the ~24.1GB of GPU-usable memory here, 11 of 11 open image models will run — the best being FLUX.1 [dev] at FP8, peaking near 19.8GB — but that model is non-commercial; FLUX.1 [schnell] is the best option you can ship.

Weight sizes are read from the real .safetensors files in each model’s Hugging Face repository. Activation memory is an approximation that varies with the attention implementation and sampler, so leave headroom. These are planning figures, not benchmarks.

What is the largest model the Apple MacBook Pro 14-inch (M3 Pro 36GB) can run?

Cohere Command R 35B (35.0B) is the largest model that fits, at roughly 21GB in Q4_K_M against ~24.1GB of GPU-usable memory. It is rated "Comfortable" here, with an estimated 4.6 tokens/sec.

How many tokens per second should I expect?

Speed depends on the model, not just the machine. On this hardware the estimates range from about 49.2 tokens/sec for the smallest models down to single digits for the largest that still fit. The estimate uses memory bandwidth (150 GB/s here) × 0.65 ÷ active weights in GB, and is not a measured benchmark.

Can the Apple MacBook Pro 14-inch (M3 Pro 36GB) run a 70B model?

Not in a usable way. A 70B model needs roughly 40GB+ at Q4_K_M before context, which exceeds the ~24.1GB of GPU-usable memory available here. A mixture-of-experts model is usually the better route on this machine, since only a fraction of its weights are active per token.

How much context can I use?

64k tokens is a sensible ceiling on this machine at Q5_K_M / Q8_0. Context is stored in the KV cache, which grows with every token and competes with the model weights for the same memory, so a longer context means a smaller model.

Can the Apple MacBook Pro 14-inch (M3 Pro 36GB) generate images with Flux or Stable Diffusion?

Yes. 11 of 11 open image models fit at 1024x1024, the best being FLUX.1 [dev] at FP8, peaking near 19.8GB. Note that FLUX.1 [dev] is licensed for non-commercial use only; FLUX.1 [schnell] is the best option you can actually ship. Higher resolutions and larger batches cost proportionally more.

Which runner works best on the Apple MacBook Pro 14-inch (M3 Pro 36GB)?

Ollama, LM Studio, AnythingLLM are all expected to run on this hardware. Ollama is the simplest starting point, and `ollama run qwen3.6:35b` is a good first model for this machine.

In-depth reads that pair with this machine — sizing memory, picking a runner, and choosing quantization for ~24.1GB of usable memory.

• Best Mac for Local LLMsMac mini M6 and M5 Pro vs Mac Studio M5 Max and Ultra.Read the guide
• Apple Silicon vs Snapdragon X for Local AIUnified memory, NPUs and what actually matters.Read the guide
• How Much RAM and VRAM Do You Need to Run Local LLMs?Model weights, KV cache and OS overhead, tier by tier.Read the guide