## Short answer: Yes — you can run it comfortably, and it's a good fit for a 64 GB M1 Max NVIDIA **Nemotron 3.5 Lightning** is a 30B-parameter Mixture-of-Experts model that activates only ~3B parameters per token, so it's fast for its size. Your M1 Max with 64 GB unified memory has plenty of headroom. ### The model's memory footprint - **Q4_K_M (4-bit):** ~23.7 GB file, ~25.8 GB needed at runtime (weights + KV cache + overhead) at 4k context ([localmodel.run](https://localmodel.run/model/nemotron-3.5-lightning-30b-a3b)) - **Q8_0 (8-bit):** ~32.6 GB file, ~34.7 GB to run ([localmodel.run](https://localmodel.run/model/nemotron-3.5-lightning-30b-a3b)) - Unsloth's own guidance: **~20 GB RAM for 4-bit, ~33 GB for 8-bit** ([unsloth.ai](https://unsloth.ai/docs/models/nemotron-3.5)) ### Fit on your M1 Max 64 GB A dedicated fit-check for exactly your machine rates it **"YES — Runs Great" (grade A)**: - ~25.4 GB required vs. **46.1 GB usable** on the M1 Max 64 GB (~55% memory used) - **~36–40 tokens/sec decode**, ~4.9s time-to-first-token (warm) - Safe context window of roughly **433K tokens** at that quantization - Rated "Runs well" across chat, coding, agentic coding, reasoning, and RAG workloads ([willitrunai.com](https://willitrunai.com/can-run/nemotron-3.5-lightning-30b-a3b-on-m1-max-64gb)) ### Important caveats 1. **MoE is faster, not lighter.** All 30B parameters' experts must stay resident in memory even though only 3B activate per token — so you still need memory for the full 30B ([localmodel.run](https://localmodel.run/model/nemotron-3.5-lightning-30b-a3b)). 2. **Context length drives memory up fast.** KV cache grows with context: ~25.8 GB at 4k, ~35 GB at 32k, and ~66.6 GB at 128k — the last would exceed your usable memory ([localmodel.run](https://localmodel.run/model/nemotron-3.5-lightning-30b-a3b)). 3. **No official NVIDIA Mac support.** NVIDIA's optimized path is NVFP4 on RTX/DGX hardware; on Mac you rely on **community GGUF/MLX builds** (Unsloth Dynamic GGUFs, llama.cpp, LM Studio, Ollama) ([ai-on-mac.com](https://ai-on-mac.com/articles/nvidia-nemotron-3-5-lightning-mac-en/)). 4. **NVFP4 ≠ 4-bit GGUF.** NVIDIA's NVFP4 checkpoint is not interchangeable with a standard 4-bit GGUF, so the ~670 tok/s figures you may see are from datacenter NVFP4 serving, not your Mac ([ai-on-mac.com](https://ai-on-mac.com/articles/nvidia-nemotron-3-5-lightning-mac-en/)). 5. **Shared memory contention.** The OS, browser, and runtime all compete for the same unified memory pool, so real-world headroom is tighter than raw capacity suggests ([willitrunai.com](https://willitrunai.com/can-run/nemotron-3.5-lightning-30b-a3b-on-m1-max-64gb)). ### How to run it - **Unsloth Desktop** (macOS app) or **llama.cpp** with Metal (Metal is enabled by default; set `-DGGML_CUDA=OFF`) ([unsloth.ai](https://unsloth.ai/docs/models/nemotron-3.5)) - **Ollama:** `ollama run nemotron-3.5-lightning:30b-a3b` - **LM Studio:** `lms get bartowski/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF` ([localmodel.run](https://localmodel.run/model/nemotron-3.5-lightning-30b-a3b)) - Recommended inference settings: thinking mode temp 0.6 / top_p 0.95; instruct mode temp 0.2 ([unsloth.ai](https://unsloth.ai/docs/models/nemotron-3.5)) ### Bottom line On an M1 Max 64 GB, expect a **solid, usable experience at Q4_K_M (~36–40 tok/s)** with a large context window — great for agentic/tool-calling and coding tasks. You can even push to Q8_0 (~33 GB) if you keep context modest. Just don't expect the datacenter NVFP4 speeds, and keep context under ~128k to avoid blowing past your memory budget. Note the model is licensed **OpenMDW-1.1 (commercial use OK)** ([localmodel.run](https://localmodel.run/model/nemotron-3.5-lightning-30b-a3b)). *Caveat: the M1 Max speed figures come from a modeling/estimation site (willitrunai.com), not a measured benchmark — treat the ~36 tok/s as an estimate.*