M5 Ultra 256GB: Which LLMs Fit? 27B to 200B+ Capacity Analysis

Understanding 256GB Unified Memory

Traditional GPUs use dedicated VRAM (e.g., RTX 5090: 32GB, RTX PRO 6000: 96GB). Apple Silicon uses unified memory — CPU and GPU share the same pool, eliminating the VRAM wall. The M5 Ultra top configuration provides 256GB unified memory , with ~250GB usable after system overhead and ~1024 GB/s bandwidth. This single machine can theoretically hold an entire "cloud-scale" large model.

Capacity Breakdown: Q4 Quantization

Local inference commonly uses Q4 (4-bit) quantization, where weights occupy roughly parameters × 0.5 bytes. The following estimates show all mainstream models fit within 256GB:

27B / 32B: ~17–20GB — trivial, leaves room for multiple concurrent models or heavy multitasking.

70B: ~40GB — the threshold from "toy" to production-grade reasoning, code understanding, long-form writing. Uses <20% of memory, leaving >200GB for context and multi-instance.

120B: ~70GB — near flagship cloud experience, handles long documents and complex multi-turn analysis. Uses ~30% of memory.

200B+: two categories:

Dense 200B+ (e.g., Qwen3-235B total params): Q4 needs 100–140GB. Fits but headroom tightens.

MoE architecture (e.g., Qwen3-235B-A22B, total 235B but only 22B active per step): weights ~130GB, inference speed feels like a 22B model — trading storage for intelligence.

One-line summary: From 27B to 200B+, Q4 quantized models all fit in 256GB unified memory. The difference is not "can it run" but "how comfortably it runs."

Critical Insight: "Fits" ≠ "Runs Smoothly" — KV Cache Overhead

Many only count model weights, but inference also requires KV Cache (Key/Value states) which grows linearly with context length. Estimates for GQA architectures:

70B model, 32K context: KV Cache adds ~5–10GB.

70B model, 128K context: KV Cache adds ~20–40GB.

The real value of 256GB is not just fitting 70B, but having half the memory left for large contexts, multiple concurrent sessions, and agent orchestration . A 64GB machine running 70B is cramped; 256GB is a spacious apartment with room for roommates.

Unified Memory vs. Multi-GPU Setups

Alternatives like dual RTX 5090 (64GB total) or dual RTX PRO 6000 (192GB) exist, but the core difference is usability :

Multi-GPU requires model sharding, PCIe bandwidth management, driver/framework compatibility.

Unified memory presents one contiguous block; frameworks (MLX, llama.cpp) load the full model directly — no sharding headaches. For those who don't want to tinker, this is a dimensionality reduction.

Who Should Buy, Who Should Skip

Worth it: Heavy local LLM researchers, AI app developers, privacy-sensitive workloads (data never leaves machine), users wanting single-machine 70B–200B without multi-GPU complexity.

Skip: Casual tinkering, coding, video editing — M4 Max (64–128GB) is plenty; savings could buy a monitor.

Caveat: 256GB version costs nearly 7× the base model. The price/performance ratio only exists when you genuinely saturate that memory .

Final Takeaway

256GB unified memory pushes the desktop local LLM ceiling to 200B+, currently the most hassle-free "local AI supercomputer" in consumer space. Remember: capacity determines how big a model you can load; context length and speed determine how pleasant it is to use .