TL;DR: The RTX 5090 has 3× the memory bandwidth of an M4 Max, so it wins tokens/sec on anything that fits in its 32GB — often by 3–5×. But it hard-stops at 32GB, and a Mac Studio M4 Max with 96–128GB of unified memory is the only sub-$4K box that loads a 70B model at all. Pick the 5090 for speed under 32GB, the Mac for capacity above it.

	Mac Studio M4 Max	RTX 5090 32GB	Used RTX 3090 24GB
Best for	Big models (70B+) that don’t fit a GPU	Fastest tok/s on 7B–30B models	Most VRAM per dollar
Memory	96GB unified (128GB config pulled)	32GB GDDR7	24GB GDDR6X
Bandwidth	546 GB/s	1,792 GB/s	936 GB/s
Price (Jul 2026)	~$2,999 base, $4K+ for 96GB	$3,000+ street	~$1,050
The catch	3× slower per token than the 5090	Can’t load a 70B Q4 model	No warranty, 350W of heat

Honest take: If your models fit in 32GB, buy the 5090 and don’t look back — nothing consumer touches its 1,792 GB/s. If you need to run 70B unquantized or juggle 100GB of context, the Mac Studio is the only quiet, single-box answer under $4,000. They’re not really competitors; they solve different problems.

Two philosophies: capacity vs bandwidth

Every local-LLM buying decision comes down to two specs, and this matchup is the cleanest illustration of both.

Whether a model runs at all is decided by memory capacity. All of a model’s weights have to sit in fast memory simultaneously. A 70B model at Q4_K_M is about 42.5GB; a 30B-class model is 18–22GB; an 8B model at Q4 is roughly 5GB. Exceed your memory even slightly and the runtime spills layers to slower storage, where throughput collapses to single digits.

How fast it runs is decided by memory bandwidth. Single-stream token generation is almost purely bandwidth-bound, and the ceiling is close to linear:

theoretical max tok/s ≈ memory bandwidth ÷ model weight size (GB)

The RTX 5090 and the M4 Max sit at opposite ends of this trade-off. The 5090 is a bandwidth monster with a small tank: 1,792 GB/s across 32GB of GDDR7. The M4 Max is the reverse — a modest 546 GB/s, but with a unified-memory pool that historically stretched to 128GB, so the CPU and GPU share one giant address space and there’s no separate “VRAM” to overflow.

That single architectural difference explains every result below.

Under 32GB: the RTX 5090 runs away

When a model fits in 32GB, this isn’t close. Plug the bandwidth numbers into the formula and the 5090’s 1,792 GB/s should deliver roughly 3.3× the M4 Max’s 546 GB/s, and real benchmarks land right there.

On Llama 3.1 8B at Q4_K_M, the M4 Max generates about 45–55 tok/s — comfortably faster than reading speed and perfectly pleasant for chat. The RTX 5090 on the same class of model posts anywhere from ~140 tok/s (Llama 3.2 8B via Ollama at longer context) up to ~260 tok/s at short context, with prefill (prompt processing) around 7,200 tok/s — a job where the Blackwell compute, not just bandwidth, pulls ahead. Even taking the conservative end, that’s 3× the Mac’s decode speed and several times its prefill.

On 30B-class mixture-of-experts models, the gap holds. Qwen3 30B-A3B (only ~3B parameters active per token) clears 100 tok/s on the M4 Max because MoE decoding only touches a fraction of the weights. On the 5090, the same architecture at Q4 runs closer to ~198 tok/s. Both machines feel instant here; the 5090 just feels more instant.

The practical takeaway: for the daily driver workloads most people actually run — a 7B/8B assistant, a 14B coding model, a 30B MoE for heavier reasoning — the 5090 is the faster machine by a wide margin, and it’s not subtle. If your workflow lives under 32GB, the Mac’s only advantages are power and noise, which we’ll get to.

Above 32GB: the wall, and where only the Mac survives

Now flip the model size. A 70B model at Q4_K_M is about 42.5GB before you add any KV cache for context. That number is larger than the 5090’s entire 32GB. So the 5090 can’t load it — not slowly, not at all, without offloading a chunk of layers to system RAM over PCIe. Once that happens, throughput on the offloaded portion drops to a few tok/s and the whole model runs at the speed of its slowest piece. In practice, a 70B model on a single 5090 is a non-starter; you’d reach for a smaller model or a heavier quant, or add a second card.

The M4 Max doesn’t hit that wall. With 96GB (or 128GB, on the configs Apple used to sell) of unified memory, a 70B Q4 model loads with room to spare, and it runs at a steady 15–25 tok/s with zero throttling and near silence. That’s slower than you’d want for a live coding loop, but it’s real, sustained, single-box inference on a model the 5090 simply refuses. Push higher — a 120B MoE, or a 70B model with a huge context window — and the Mac remains the only consumer machine in this price bracket that even boots it.

This is the whole reason the matchup exists. The 5090 wins the race up to 32GB. Past 32GB, it’s not slower than the Mac — it’s absent.

The 128GB asterisk nobody mentions

Here’s the wrinkle the spec-sheet comparisons miss, and it’s the one that actually changes the buying decision in July 2026.

The M4 Max chip supports up to 128GB of unified memory. But the same memory shortage that shoved the RTX 5090 to $3,000+ and pushed used RTX 3090 prices up hit Apple too. As of mid-2026, Apple pulled the 128GB and 256GB memory options from the Mac Studio configurator — the M4 Max now tops out at 96GB of unified memory on new orders, and Apple raised Mac prices roughly $500 across the line in June 2026. The base M4 Max Mac Studio (36GB) starts near $2,999; a 96GB build lands well above $4,000.

For LLM buyers, 96GB is still plenty — it holds a 70B Q4 model, its KV cache, and the OS with headroom, because macOS lets the GPU claim about 75% of unified memory by default (and you can raise that ceiling). But if your plan depended on 128GB+ to run a 100B+ model, your only new-hardware routes are a used 128GB M4 Max on the secondhand market or a step up to an M3 Ultra Mac Studio — which was hit by the same cuts at its top tiers. Don’t assume the 128GB config is a phone call away; verify current availability before you budget around it. This is the same GDDR7/LPDDR5X crunch we tracked in the RTX 5090 price-hike breakdown, now biting Apple’s SKUs too.

Power, noise, and the desk it lives on

The RTX 5090 carries a 575W TDP. In sustained LLM inference it won’t peg the full gaming draw, but you’re realistically looking at 400–550W from the card alone under a long decode, with transient spikes reported up to 650–740W over a few milliseconds — which is why the PSU guidance for these cards starts at 1,000W. Add a CPU, drives, and fans and a 5090 rig pulls 600–750W at the wall under load, dumps that heat into the room, and spins fans to move it.

The Mac Studio M4 Max is rated for 480W maximum continuous draw, but that’s the power-supply ceiling, not the working number. Real torture tests put the M4 Max around 200W under sustained load, and typical LLM inference sits well below that. It’s effectively silent and cool enough to keep on a desk in a quiet room. Over a year of heavy use, the electricity difference is real money — we did that math in the home AI server power-bill guide — and the ergonomic difference (no fan roar, no space-heater effect) is the kind of thing that decides whether you actually leave the machine running 24/7.

Software: CUDA maturity vs MLX momentum

The 5090 inherits the entire NVIDIA stack: CUDA, PyTorch, vLLM, TensorRT-LLM, and every fine-tuning toolchain that assumes an NVIDIA GPU. If you want to train, run QLoRA, serve multiple concurrent users with vLLM, or use anything cutting-edge on release day, the 5090 (Blackwell, needs recent CUDA 12.8+ builds) is the path of least resistance. It’s the platform everything targets first.

Apple Silicon runs on llama.cpp with the Metal backend, plus Apple’s own MLX framework, which has become genuinely fast — MLX is often 20–90% quicker than llama.cpp for decode on models under ~14B, and Ollama’s MLX backend now delivers big speedups with no configuration. For inference, chat, and local coding with Ollama or LM Studio, the Mac experience is smooth and well-supported. Where it lags is training and high-concurrency serving: vLLM-class throughput and the newest research code still land on CUDA first. If you’re building coding tooling on top of a local backend, our sister site aicoderscope.com covers which editors and agents play nicely with each, and aifoss.dev has the open-source self-hosting setup guides.

Price, and the used-3090 spoiler

Sticker to sticker, these two land close: a street RTX 5090 is $3,000+, and a 96GB Mac Studio M4 Max is $4,000+. Neither is cheap, and both are inflated by the memory shortage.

Which is exactly why the used RTX 3090 keeps crashing this party. At ~$1,050 for 24GB and 936 GB/s, it runs 7B–30B models faster than the Mac (its bandwidth is nearly 2× the M4 Max’s) at a third of either machine’s price, and two of them in one box gets you 48GB for less than a single 5090. It won’t match the 5090’s raw speed or the Mac’s capacity, but for most people the honest question isn’t “5090 or Mac” — it’s “do I actually need either, or does a 3090 do the job?” We make that case in full in the used RTX 3090 value guide.

And if you only need a big model occasionally, renting beats buying: a cloud H100 or A100 on RunPod runs a 70B model faster than any of these for a few dollars an hour, with no memory-shortage markup and nothing to resell later.

Who should buy which

Buy the RTX 5090 if: your models fit in 32GB (7B–30B is your zone), you want the fastest possible tokens/sec, you’ll fine-tune or serve with vLLM, or you already own a PC and just need the card. You’re buying raw speed and the CUDA ecosystem.

Buy the Mac Studio M4 Max if: you need to run 70B+ models on one machine, you want a silent always-on box, you value low power draw, or you’re already in the Apple ecosystem and want unified memory to spill into. You’re buying capacity and calm. Note the 96GB ceiling on new orders — if you were counting on 128GB, confirm availability first. (If you’re weighing Mac tiers, the Mac Studio M4 Max vs Mac Mini M4 Pro comparison and the M5 Max MacBook analysis go deeper.)

Buy neither if: a used RTX 3090 covers your model sizes (it covers most), or your big-model needs are occasional enough to rent. For a full breakdown of which model fits which Blackwell card, see our best LLM for every RTX 50-series GPU guide.

FAQ

Can the RTX 5090 run a 70B model? Not fully. A 70B model at Q4_K_M is ~42.5GB, which exceeds the 5090’s 32GB. You’d have to offload layers to system RAM, which drops throughput to single digits, or use a smaller/heavier-quantized model. For 70B on one machine, the Mac’s 96GB+ unified memory is the answer.

Is the M4 Max slower than the RTX 5090 for local LLMs? Yes, on any model that fits both — typically 3–5× slower per token, because the 5090 has 3× the memory bandwidth. The Mac only “wins” on models too big for 32GB, where the 5090 can’t compete because it can’t load them.

Why was the 128GB Mac Studio discontinued? The 2026 memory shortage. Apple pulled the 128GB and 256GB unified-memory options and capped the M4 Max Mac Studio at 96GB on new orders, the same crunch driving GDDR7 GPU prices up. Check current Apple configuration options before assuming 128GB is available.

Which uses less power? The Mac, by a lot. The 5090 has a 575W TDP and pulls 400–550W under sustained inference; the M4 Max stays around 200W or less and runs near-silent.

What about the M3 Ultra or M5? The M3 Ultra Mac Studio offers more bandwidth and (formerly) higher memory ceilings but was also hit by the config cuts at its top tiers. An M5-generation Studio is rumored but not shipping as of July 2026 — see our M5 Max analysis.

Sources

• Mac Studio M4 Max 128GB local LLM testing — CraftRigs
• RTX 5090 tokens per second Ollama benchmarks — Markaicode
• RTX 5090 for Local AI: 32GB and Blackwell — Runyard
• M5 Max vs M4 Max local LLM showdown — LLM Check
• 2026 Mac Studio M5 release, specs, and RAM delay news — Macworld
• RTX 5090 and RTX 5080 power draw revised to 575W — Tom’s Hardware
• Mac Studio power consumption and thermal output — Apple Support
• Apple Silicon LLM benchmarks 2026 — LLM Check

Prices and availability as of July 2026 and move weekly during the ongoing memory shortage. Verify current configuration options and street prices before buying.

Recommended Gear

• RTX 5090 — 32GB GDDR7, 1,792 GB/s; the fastest consumer card for models under 32GB.
• Mac Studio M4 Max — 546 GB/s unified memory, up to 96GB new; the quiet single-box answer for 70B+ models.
• Used RTX 3090 — 24GB, 936 GB/s, ~$1,050; still the value pick for 7B–30B models.