M4 Max and M3 Ultra for Local LLMs: Apple Silicon in 2026

📚 More on this topic: Best Local LLMs for Mac · Mac vs PC for Local AI · Running LLMs on Mac M-Series · VRAM Requirements

If you follow Apple Silicon and local AI, you were expecting 2025 to bring the M4 Ultra — a 256GB+ chip that would make the Mac Studio the definitive local AI workstation. It didn’t happen. The M4 Max chip lacks UltraFusion support, the die-to-die interconnect that combines two Max chips into one Ultra. Apple hasn’t said whether this is permanent or just delayed.

What they shipped instead: a Mac Studio with the M4 Max (current gen) and the M3 Ultra (previous gen). A newer chip with less memory, or an older chip with more. For local AI, that tradeoff matters more than any benchmark.

Then 2026 made it worse. The global DRAM shortage — driven substantially by local-AI demand, with reporting singling out the early-2026 OpenClaw boom — didn’t just delay Macs, it deleted configurations. Apple cut the Mac Studio’s memory ceilings hard: the M4 Max now tops out at 64GB (from $2,499), the M3 Ultra at 96GB ($5,299, up $1,300 in June). The 128GB and 192GB Studio configs this guide used to recommend are gone. If you need 128GB now, it isn’t a desktop at all — it’s the M5 Max MacBook Pro. So the numbers below are what you can actually buy in mid-2026, not the lineup Apple shipped in spring.

The Specs That Actually Matter

Memory bandwidth determines LLM inference speed. Every token requires reading through the model’s weights in memory. More bandwidth, more tokens per second. More GPU cores barely help for this workload. This is worth internalizing before you shop: RAM tells you if a model loads, but bandwidth tells you how fast it runs — and most Mac buying guides lead with RAM and bury bandwidth.

Here’s the whole Apple bandwidth picture, including the laptops, because the desktop no longer holds the top of it:

The M3 Ultra has 50% more bandwidth than the M4 Max. That translates directly to ~40-50% faster token generation on the same model. But the M4 Max has a newer architecture with better per-core IPC and Thunderbolt 5. For prompt processing — the initial computation before tokens start flowing — the M4 Max’s architectural improvements partially close the gap. And note the trap in that table: the 96GB M3 Ultra moves tokens faster than the 128GB M5 Max laptop, despite holding less memory. More RAM does not mean more speed.

This is the same reason an M3 Max generates tokens faster than an M4 Pro despite being older. Bandwidth trumps architecture generation for this workload.

M4 Max: Current Gen, Now Capped at 64GB

After the 2026 cuts, the M4 Max Mac Studio is a two-config machine:

• $2,499: 14-core CPU, 32-core GPU, 36GB, 512GB SSD
• BTO: up to 40-core GPU and 64GB unified memory (the old 128GB build-to-order is gone)

That 64GB ceiling changes the story this guide used to tell. The M4 Max was “the first sub-$4,000 Apple machine that comfortably runs 70B models” — on its 128GB config. That config no longer exists. On 64GB, a 70B at Q4 (~40GB) still loads, but you’re left with ~20GB for macOS and context. It runs; it isn’t comfortable. If a roomy 70B is the goal, the 96GB M3 Ultra is now the smallest Mac that does it without pressure.

M4 Max Benchmarks (MLX, Q4 Quantization)

Speeds are community reports on M4 Max hardware — we bench on a 3090, not Apple silicon, so treat these as guidance math rather than our own measurements.

These numbers use MLX, Apple’s native ML framework. That used to mean a big gap over Ollama — but Ollama switched to a native MLX runner on Apple Silicon in 0.19 (March 2026), so the old 30-50% penalty mostly closed. MLX-LM still keeps a smaller edge (roughly 10-25%, wider on M5 and MoE models). More on that below.

At 15-18 tok/s, a 70B is usable for interactive chat — faster than you read — but on a 64GB M4 Max you’re loading it with almost nothing to spare. For 8B-32B models the M4 Max is overkill on speed; you’re buying it for the headroom, and that headroom just shrank.

M3 Ultra: More Memory, More Bandwidth, Last Gen

The M3 Ultra Mac Studio now starts at $5,299 — up $1,300 in the June 2026 hikes — with a 28-core CPU, 60-core GPU, and 96GB unified memory. That 96GB is the whole story of the 2026 cuts in one number: this chip used to configure to 256GB, then 512GB at the very top. Both are gone. The chip itself is from 2024, but its raw bandwidth advantage over the M4 Max is still the reason to buy it.

M3 Ultra Benchmarks (MLX, Q4 Quantization)

Community-reported speeds on M3 Ultra hardware; guidance math, not our bench.

The M3 Ultra’s advantage is capacity and speed relative to the M4 Max: 96GB at 819 GB/s runs a 70B comfortably where 64GB can’t, and runs everything else 40-50% faster. A 70B at 25-30 tok/s versus 15-18 tok/s is a real difference in interactive chat.

What changed in 2026 is the ceiling. This is no longer the machine for 120B+ models or two 70B models resident at once — 96GB can’t hold those. If that was your reason to want an Ultra, the shortage took it away, and no current Mac replaces it. The question now is whether 96GB comfortably running one 70B is worth $5,299 and a previous-generation chip.

MLX: The Software That Makes It Work

This section used to open with “Ollama leaves 30-50% of your speed on the table.” That’s no longer true, and the reason is a 2026 change worth knowing: Ollama switched to a native MLX runner on Apple Silicon in version 0.19 (March 2026). It’s no longer a llama.cpp wrapper on Mac — it taps the same Apple framework the fast path always used. On an M5 Max, that upgrade roughly doubled Ollama’s decode speed on a 35B MoE. So the old “Ollama is slow on Mac” critique is mostly dead. Upgrade to 0.19 or later and you get most of the speed for free.

MLX is Apple’s machine learning framework, purpose-built for the Metal GPU API and unified memory architecture. It still holds an edge over the llama.cpp path, just a smaller one now:

• Roughly 10-25% faster token generation than llama.cpp on 14B+ models, wider on M5 hardware and MoE architectures
• Much wider on some MoE models — one Qwen3-Coder-30B-A3B test hit ~130 tok/s under MLX versus ~43 under the llama.cpp backend
• LM Studio defaults to MLX on Mac — the easiest way to get the edge with a GUI
• MLX-LM (command line) gives direct access for power users

The reason is focus. llama.cpp supports dozens of hardware backends: NVIDIA, AMD, Intel, CPU, Vulkan, Metal. MLX only targets Apple Silicon, and on the M5 it uses the new per-core Neural Accelerators that llama.cpp doesn’t tap yet. That specialization keeps it ahead.

For a Mac Studio in this price range, MLX-LM is still the fastest path. But if you’d rather run one command, Ollama 0.19+ now gets you most of the way there — the gap that used to justify the extra setup has narrowed to the last few tok/s.

Mac Studio vs PC for Local AI

I’m using the 96GB M3 Ultra as the Mac column now, because after the 2026 cuts it’s the Studio that runs a 70B comfortably — the 64GB M4 Max only loads one with no room to breathe.

The Mac advantage: memory capacity. 96GB of unified memory runs 70B models natively. On PC, that requires dual GPUs with NVLink — which limits you to the used RTX 3090, the only consumer card that supports it — or $10,000+ datacenter GPUs. The Mac also runs near-silent at a fraction of the power.

The PC advantage: bandwidth, though the M3 Ultra narrows it. An RTX 4090’s 1,008 GB/s is only about 1.2x the Ultra’s 819. For models that fit in 24GB VRAM, discrete GPUs are still faster. A dual 3090 NVLink setup pushes ~1,872 GB/s combined — about 2.3x the M3 Ultra. The Mac’s price climbed hard in 2026, so this is a closer call on value than it was in spring.

The comparison breaks down at model size boundaries. The Mac doesn’t win on speed. It wins on what fits. See our Mac vs PC guide for the full breakdown.

→ Check what fits your hardware with our Planning Tool.

Power and Silence

This is where Apple Silicon genuinely has no competition.

At US average electricity rates ($0.18/kWh). A Mac Studio M4 Max running a 70B model draws about 60W. A dual-3090 PC doing the same work draws 700W+. The annual electricity difference is $400-$500. Over three years, that’s $1,200-$1,500 — enough to significantly offset the Mac’s higher hardware cost.

And the Mac Studio at 60W under AI load is near-silent. You can run it as an always-on inference server in a bedroom or office without hearing it. Try that with dual RTX 3090s.

The Verdict

The 2026 cuts inverted the old recommendation. There’s no 128GB M4 Max sweet spot anymore, so here’s the choice as it actually stands.

Buy the M4 Max Mac Studio (64GB, $2,499) if:

• You mostly run 8B-32B models and want current-gen silicon with Thunderbolt 5
• You’ll run a 70B only occasionally and can live with a tight fit and short context
• Budget tops out around $2,500
• You value silence and low power for always-on inference

Buy the M3 Ultra Mac Studio (96GB, $5,299) if:

• You want a 70B running comfortably, with real room for context
• Raw speed matters — it’s 40-50% faster than the M4 Max, and at 819 GB/s the fastest Mac Apple sells
• You can absorb the June hike (the same machine was $3,999 in spring)

Look past the Studio entirely if:

• You need 128GB — that’s the M5 Max MacBook Pro now, not a desktop at all
• Your workload is 8B-32B — the M4 Pro Mac Mini costs far less
• Speed per token beats capacity — discrete GPUs have 1.2-2.3x the bandwidth
• You mainly do image generation or fine-tuning — those compute-bound jobs favor NVIDIA

The honest summary: the machine this guide used to recommend, an M4 Max at 128GB for comfortable 70B, no longer exists. For a 70B without compromise, the 96GB M3 Ultra is now the entry desktop, and it costs $5,299. For 128GB you buy a laptop. The missing M4 Ultra might still arrive with M5, but Apple isn’t saying — and after a year of memory cuts, don’t plan around it.

Last updated July 17, 2026 for Apple’s 2026 memory cuts: the M4 Max Mac Studio now caps at 64GB (from $2,499) and the M3 Ultra at 96GB ($5,299), both older configs (128GB M4 Max, 192GB/256GB M3 Ultra) discontinued. Corrected M3 Ultra bandwidth to 819 GB/s, added the full Apple bandwidth table, and updated the Ollama/MLX gap now that Ollama 0.19 runs a native MLX runner on Apple Silicon.