M5 Ultra 256GB vs RTX 5090 32GB: Local LLM Inference Compared

Hardware Foundations

M5 Ultra (Mac Studio, 256GB) : 256GB unified memory (~250GB usable after OS), ~1.2 TB/s memory bandwidth, 36-core CPU + 80-core GPU, running Apple's MLX / oMLX frameworks.

RTX 5090 : 32GB GDDR7 VRAM, 1792 GB/s VRAM bandwidth, 21,760 CUDA cores + 4th-gen Tensor Cores, 575W TDP, backed by the dominant CUDA ecosystem.

The core difference: RTX 5090 has ~50% higher bandwidth, but M5 Ultra offers 8× the memory capacity. The 5090 is a fast workbench; the M5 Ultra is a large workshop — tasks that fit on the workbench run faster there, but tasks that don't fit force the workbench to fetch tools from a distant warehouse over PCIe, while the workshop keeps everything at hand.

Capacity: The Hard Limit

Local inference commonly uses 4-bit (Q4) quantization, where weights require roughly parameters × 0.5 bytes. Mapping four model tiers onto both machines:

27B / 32B : Both handle easily. Weights ~17–20GB; RTX 5090 leaves 10+ GB for context; M5 Ultra uses a fraction of its memory.

70B : The watershed. Q4 ~40GB — RTX 5090's 32GB cannot hold it . Options: dual 5090 (64GB, requires model sharding) or offload layers to system RAM over PCIe — speed collapses (detailed below). M5 Ultra loads fully with 200GB+ to spare, enabling long contexts freely.

100B+ : M5 Ultra exclusive. Example: Qwen3-235B-A22B MoE at Q4 ~130GB — dual 5090 (64GB total) also cannot hold it . M5 Ultra 256GB loads natively. KV cache for 70B at 128K context adds another 20–40GB; headroom matters for large-context and agent workloads.

Inference Speed: Tokens per Second

Real-world measurements (MacStories) and bandwidth estimates, varying with framework, quantization, and context length:

27B / 32B : RTX 5090 slightly faster. Same Qwen3.8 27B Q4 short prompt: 5090 59 tok/s vs M5 Ultra 48 tok/s (~23% gap, far smaller than bandwidth gap because both are comfortable). Smaller models widen the lead: 7B Q4 ~160 tok/s (5090) vs ~135 tok/s (Mac).

70B : Situation reverses. 5090 offloads to system RAM; measured generation speeds at 64K/128K/256K context drop to 4.6 / 2.9 / 1.5 tok/s — practically unusable. M5 Ultra, with full unified-memory residency, sustains 20–25 tok/s , sufficient for daily interaction.

100B+ : Only one side runs. 235B-class MoE on M5 Ultra ~14 tok/s (estimated), interactive; 5090 side is "no solution."

Why is offload so disastrous? PCIe effective bandwidth is only a few percent of unified memory bandwidth. Every generated token forces the missing weight shards to be "moved" from system RAM across the PCIe bus. Beyond the VRAM wall stands a PCIe wall.

Power Consumption: Appliance vs Power Hog

RTX 5090 : Single card 575W; full system 700W+, requiring 1000W PSU and serious case airflow. Under load the room feels warmer.

M5 Ultra : Full system load ~200–300W; idle single-digit watts. Running 4-bit models daily, fans are nearly inaudible — MacStories noted the 5090 PC "heated the whole room."

In "always-on" scenarios (7×24 local agents, long-context analysis), the annual electricity cost difference reaches thousands of yuan, not counting cooling and noise. M5 Ultra is the only machine that can sit quietly on a desk as an appliance.

CUDA vs MLX: The Real Moat

CUDA is the ecosystem king. TensorRT-LLM, vLLM, PyTorch, llama.cpp full stack; training, fine-tuning, multi-GPU scaling, production deployment all default to CUDA. 21,760 CUDA cores + Tensor Cores make the 5090 the speed benchmark for any task that fits in VRAM.

MLX is built for unified memory. Zero-copy — CPU and GPU share the same physical memory, no data movement, instant start. Community fork oMLX adds MoE optimization and MTP speculative decoding, further boosting M5 Ultra's long-context performance.

Each has clear weaknesses:

MLX : Ecosystem far smaller than CUDA; weak training/fine-tuning support; "multi-GPU" only exists inside the chip (UltraFusion).

CUDA : The VRAM wall is a law of physics — beyond 32GB, CUDA has no answer.

One truth: CUDA's strength wins on "what fits"; MLX's strength wins on "what doesn't fit but still runs."

Conclusion: Who Should Buy Which

Buy RTX 5090 (or multi-GPU) if:

You mainly run 27B/32B models and want maximum speed — 59 vs 48 tok/s, fast is fast.

You need training, fine-tuning, batch inference, or production serving — CUDA ecosystem has no substitute.

You also play 4K games.

Buy M5 Ultra (256GB minimum) if:

You want to run 70B–200B models on a single machine without multi-GPU sharding hassle.

You need 7×24 always-on: silent, low power, desk-friendly.

Data sensitivity requires model and context never leave the box.

Bottom line: If your model fits in 32GB, the 5090 is better value; if it doesn't, the M5 Ultra is currently the only hassle-free solution. They are not substitutes but boundaries of two product lines — first check the size of the models you run daily, then decide where to spend.

Speed data sourced from MacStories real-world tests and community bandwidth estimates; varies with framework, quantization, and context length — for relative reference only.