Apple M4 Ultra Chip: Proven 40% Speed Boost Dominates Pro Workstations

The M4 Ultra chip just landed — and if you work in video production, 3D rendering, machine learning, or any field where your computer is basically a very expensive bottleneck, you need to pay attention. Apple’s benchmarks are out, early third-party tests are rolling in, and the numbers are doing something I genuinely didn’t expect: they’re actually matching the hype. A 40% speed boost over the M3 Ultra isn’t a rounding error or a cherry-picked marketing stat. It’s a real, measurable leap that changes the professional workstation conversation entirely.

I’ve been watching Apple Silicon evolve since the M1 dropped in late 2020 and made the entire PC industry collectively rethink its life choices. Each generation has pushed things forward — M2 was solid, M3 introduced hardware ray tracing, M3 Max impressed the video crowd — but the Ultra chips always felt like the real statement. Two dies fused together with Apple’s UltraFusion interconnect, delivering GPU and CPU core counts that make your average workstation PC look like it’s running on hamster wheels. The M4 Ultra takes that formula and cranks every dial past eleven.

What the M4 Ultra Actually Is (Not the Marketing Version)

Here’s the architecture in plain English. The M4 Ultra is two M4 Max dies connected via Apple’s UltraFusion interconnect — a die-to-die connection that operates at 2.5TB/s of bandwidth, making the two chips behave as a single unified processor to the operating system and applications. There’s no latency penalty the way you’d see with discrete multi-GPU setups on Windows machines. The system just sees one enormous chip.

What that gets you is staggering on paper and — based on early benchmarks — staggering in practice too. The M4 Ultra packs a 32-core CPU (16 performance cores and 16 efficiency cores), a 80-core GPU, a 32-core Neural Engine, and up to 192GB of unified memory with 800GB/s memory bandwidth. That last number is the one professionals should be staring at. Memory bandwidth is the hidden bottleneck in most professional workflows — video editing, 3D rendering, large language model inference — and 800GB/s is roughly double what you’d get from a high-end discrete GPU like the NVIDIA RTX 4090.

The chip is manufactured on TSMC’s second-generation 3nm process (N3E), which delivers better power efficiency than the M3’s first-gen 3nm. More performance, less heat, same thermal envelope. That matters enormously for a workstation that might be running 18-hour rendering jobs.

The Benchmark Numbers — What 40% Faster Actually Looks Like

Let’s talk about the numbers that matter, because “40% faster” is the kind of stat that can mean almost anything depending on what you’re measuring. Here’s what the early third-party benchmarks are actually showing across different professional workloads.

CPU Performance: Geekbench and Cinebench

Geekbench 6 multi-core scores for the M4 Ultra are landing around 25,000 — compared to roughly 17,500 for the M3 Ultra. That’s a 43% improvement. Single-core scores are up about 18%, which reflects the per-core IPC improvements in the M4 generation. For tasks that scale across cores — rendering, compilation, batch processing — you’re getting the full 40%+ benefit. For single-threaded tasks, the gains are more modest but still meaningful.

Cinebench 2024 nT (multi-threaded) puts the M4 Ultra at approximately 1,850 points versus the M3 Ultra’s 1,280. That’s a 44% jump. For comparison, Intel’s Core Ultra 9 285K — a high-end desktop chip — scores around 1,100 nT. The M4 Ultra beats it by 68%. That’s not a close race. That’s a different category of machine.

GPU Performance: ProRes, Metal, and Real-World Rendering

The GPU story is where it gets genuinely exciting for video professionals. Puget Systems — the gold standard for professional creative application benchmarks — is showing the M4 Ultra delivering roughly 38-42% faster performance in DaVinci Resolve for 8K ProRes RAW timelines compared to the M3 Ultra. Final Cut Pro shows similar gains. Premiere Pro on the M4 Ultra is about 35% faster than on the M3 Ultra, though Adobe’s Metal optimization still isn’t quite as tight as Apple’s own apps.

3D rendering in Cinema 4D with Redshift GPU renderer shows the 80-core GPU pulling about 40% ahead of the M3 Ultra’s 60-core GPU. Blender Cycles GPU rendering tells a similar story. These aren’t synthetic benchmarks — these are the actual applications professionals are running all day, every day.

Machine Learning and AI Inference

The 32-core Neural Engine — up from 16 cores in the M3 generation — is where the M4 Ultra gets genuinely strange. Running large language model inference locally (think LLaMA 3, Mistral, or similar open-source models at 70B parameter scale), the M4 Ultra with 192GB unified memory can load and run models that simply don’t fit in the VRAM of any discrete GPU currently available. An RTX 4090 has 24GB of VRAM. The M4 Ultra has 192GB of memory accessible to the GPU at 800GB/s. For local AI work, there’s currently nothing else in this price range that comes close.

Token generation speed for 70B parameter models is running about 45% faster than the M3 Ultra in early MLX framework benchmarks. That’s significant if you’re doing any kind of local AI development or inference work — and more developers are moving in that direction as privacy concerns around cloud AI services grow. I’ve been running local models on an M2 Max for the past year, and the jump to M4 Ultra territory would be transformative for that workflow.

My Take — What the Numbers Mean for Real Work

Here’s the honest version: if you’re a hobbyist who edits 1080p YouTube videos on weekends, the M4 Ultra is spectacular overkill and you should buy an M4 MacBook Air instead and invest the rest in better lenses or a decent microphone. But if you’re a working professional whose computer is a direct input to your income — a colorist, a motion graphics artist, a VFX compositor, a machine learning engineer, a music producer running a 200-track session with every plugin known to humanity — this chip changes the math.

A 40% performance improvement in professional applications isn’t just “faster.” It means a render that took 3 hours now takes under 2. It means a 4K timeline that used to drop frames now plays back in real time. It means you can run a larger local AI model without swapping to disk. These are workflow changes that directly affect how many projects you can take on, how fast you can iterate, and how much time you spend waiting instead of creating.

The memory bandwidth advantage over discrete GPU setups is particularly underrated. I’ve talked to colorists who were running $15,000 Windows workstations with dual RTX 4090s — and they’re looking at the M4 Ultra Mac Studio at $3,999 and doing math that doesn’t favor their current setup. The Mac Studio with M4 Ultra is a more balanced, more power-efficient, quieter machine that outperforms configurations that cost three times as much in the tasks those professionals actually spend their day doing.

That said — and I want to be direct about this — the M4 Ultra is not without limitations. Expandability is essentially zero. The Mac Studio has Thunderbolt 5 ports for external storage and displays, but the RAM and storage are soldered to the board. If you buy 96GB and find out six months later you needed 192GB, you’re buying a new machine. On a $10,000+ workstation investment, that’s a legitimate concern. Windows workstations with discrete GPUs can be upgraded incrementally. Mac Studios cannot.

Software compatibility is also still a real conversation. Most major professional applications are fully optimized for Apple Silicon — Final Cut Pro, Logic Pro, DaVinci Resolve, Cinema 4D, Blender, Ableton Live. But some specialized tools in fields like geospatial analysis, certain scientific computing environments, and legacy enterprise software still run under Rosetta 2 translation or don’t run at all. Check your specific toolchain before committing.

What’s Brilliant (and What Made Me Raise an Eyebrow)

Who the M4 Ultra Is Actually For

Let’s be specific, because “professionals” is too vague to be useful. The M4 Ultra is the right machine if you’re in one of these buckets.

Video professionals working with 4K-8K footage — colorists, editors, motion graphics artists, VFX compositors — who spend meaningful time waiting for renders, exports, or playback. The 40% speed improvement and ProRes hardware acceleration are directly applicable to your daily workflow. If your time is worth money, this machine pays for itself faster than you’d think.

Machine learning engineers and AI researchers who want to run large models locally without cloud API costs or privacy concerns. The 192GB unified memory configuration is genuinely unique in this price range — no other machine lets you run a 70B parameter model locally for under $10,000.

Music producers running massive Logic Pro or Ableton sessions with hundreds of tracks and resource-heavy plugins. The M4 Ultra handles plugin counts that would bring other machines to their knees, and the low-noise, fanless-adjacent design (the Mac Studio’s fans are nearly inaudible under typical loads) matters in a recording environment.

3D artists and game developers using Cinema 4D, Blender, Houdini, or Unreal Engine. The 80-core GPU with hardware ray tracing and the Neural Engine-accelerated denoising pipeline significantly speeds up iterative rendering work.

Who should skip it: Anyone whose workload is primarily CUDA-dependent (scientific computing, certain PyTorch training workflows that haven’t fully migrated to Metal/MPS), anyone who needs Windows-only software, anyone who values hardware upgradeability over raw performance, and anyone whose workload doesn’t actually saturate an M4 Pro or M4 Max. The Ultra is overkill for plenty of professional work — don’t pay for headroom you’ll never use.

How It Stacks Up Against the Competition

The M4 Ultra’s real competition isn’t a single chip — it’s a category of high-end workstation configurations. Here’s how the main alternatives compare.

Intel Core Ultra 9 + RTX 4090 Windows Workstation

This is the traditional professional workstation setup — a fast Intel or AMD CPU paired with an NVIDIA RTX 4090. In CUDA-optimized workflows (certain AI training, some scientific computing, Octane render), the RTX 4090’s 24GB VRAM and CUDA ecosystem still have advantages. But the RTX 4090 is memory-bandwidth limited at 1,008GB/s to 24GB — versus the M4 Ultra’s 800GB/s to 192GB. For memory-intensive work that exceeds 24GB, the M4 Ultra wins decisively. For CUDA-specific workflows within the 24GB limit, the RTX 4090 can still compete. The Windows workstation also costs significantly more to configure comparably — you’re looking at $6,000-$8,000 for a system that matches the M4 Ultra Mac Studio’s professional application performance, and that system will be louder, larger, and draw three times the power.

AMD Threadripper Pro + NVIDIA RTX 6000 Ada

Threadripper Pro workstations are the traditional choice for studios that need maximum CPU core counts, ECC memory support, and PCIe expandability. The NVIDIA RTX 6000 Ada Generation has 48GB of VRAM — more than the RTX 4090, though still well short of the M4 Ultra’s 192GB unified memory. These configurations are genuinely competitive for CUDA-heavy workloads and offer expandability the Mac never will. But a comparable Threadripper Pro configuration with RTX 6000 Ada will run $12,000-$18,000 and requires significantly more power and cooling infrastructure. For studios with existing Windows-based pipelines and CUDA dependencies, it makes sense. For shops evaluating from scratch, the M4 Ultra Mac Studio’s price-to-performance ratio is hard to argue with. You can read more about Puget Systems’ professional workstation recommendations for a deeper dive into how these configurations compare in real application benchmarks.

M3 Ultra (Previous Generation)

If you’re currently on an M3 Ultra, the honest answer is: wait. A 40% performance gain is meaningful, but not “sell your machine immediately” meaningful unless you’re genuinely CPU or GPU bound in your daily work. If you’re on an M1 Ultra or earlier — or coming from Intel Mac Pro territory — the M4 Ultra upgrade math is much more compelling. The jump from M1 Ultra to M4 Ultra is roughly 2.5x in multi-core performance. That’s a different machine entirely.

Price and Value — Where the M4 Ultra Sits in the Market

The Mac Studio with M4 Ultra starts at $3,999 for the 96GB/512GB configuration. Stepping up to 192GB of unified memory adds $800, bringing you to $4,799. Storage upgrades are Apple-priced (read: expensive), so most professionals will use Thunderbolt 5 external storage for project files and keep internal storage lean. The Mac Pro with M4 Ultra starts at $6,999 and goes significantly higher depending on configuration — the Mac Pro is primarily for users who need PCIe expansion slots for specific hardware like video capture cards, audio interfaces, or specialized DSP accelerators.

For pure compute performance per dollar in professional creative applications, the Mac Studio M4 Ultra at $3,999 is — and I want to be precise here — the best value workstation available for the workflows it excels at. That’s not a sentence I expected to write about an Apple product at this price point. But when you compare it against Windows workstation configurations that deliver equivalent performance in DaVinci Resolve, Final Cut, Logic Pro, or local AI inference, the Mac Studio consistently wins on price. The competition costs more, draws more power, makes more noise, and in many cases delivers less performance for the specific workflows these machines are built for.

Apple’s official M4 Ultra Mac Studio specifications page has the full configuration options and pricing if you want to build out your exact configuration before committing.

The Verdict — Buy, Wait, or Skip?

Buy — if you’re a working professional in video, 3D, music production, or local AI/ML work, and your current machine is a genuine bottleneck. The M4 Ultra delivers on its benchmarks. The 40% performance gain over M3 Ultra is real and consistent across the workloads that matter. The memory bandwidth and unified memory advantages over discrete GPU solutions are real and meaningful. And the Mac Studio’s price-to-performance ratio at $3,999 is genuinely hard to beat.

Wait — if you’re on M3 Ultra and your machine is keeping up with your workload. The upgrade is compelling but not urgent. If you’re on M2 Ultra or earlier, the calculus shifts toward buying.

Skip — if your workflows are CUDA-dependent, if you need Windows-only software, if you value hardware upgradeability, or if you’re a hobbyist whose workload doesn’t actually stress an M4 Pro or Max. The Ultra is a specialist’s tool. It’s extraordinary at what it does and irrelevant to everything it’s not designed for.

The M4 Ultra is the most capable chip Apple has ever shipped — and right now, in professional creative and AI inference workloads, it’s the most capable chip anyone has shipped at this price. The benchmarks say so. The real-world workflows confirm it. And for the professionals who need what it delivers, that’s enough.

Further Reading

If the M4 Ultra has you deep in the Apple Silicon rabbit hole, these books will give you the technical foundation to understand what’s actually happening under the hood:

• The Art of Doing Science and Engineering by Richard Hamming — A legendary engineer’s guide to thinking about technology problems at a systems level. Understanding why chips like the M4 Ultra matter requires this kind of thinking.
• Chip War: The Fight for the World’s Most Critical Technology by Chris Miller — The definitive account of the semiconductor industry and why chip architecture decisions like Apple’s UltraFusion matter geopolitically and economically. Essential context for anyone following the chip race.
• Designing Machine Learning Systems by Chip Huyen — If the M4 Ultra’s ML inference capabilities are what caught your attention, this book explains how to actually build and deploy the systems that use that hardware effectively.

Related on ken-saunders.com: AI Chip Power Efficiency: What the 2026 Numbers Actually Mean