M5 Ultra 64 vs 80 Core GPU: What ¥9750 Buys – Not Faster LLM Decoding

The new Mac Studio sparked a flood of questions: M5 Ultra 64-core GPU vs 80-core GPU, with a mainland China price gap of exactly ¥9750 (roughly $1,350). What does that extra money actually buy? This analysis breaks down specs, benchmarks, and real-world scenarios without hype.

Conclusion: The ¥9750 Doesn't Buy "Faster"

In one sentence: Both M5 Ultra versions share 1.2TB/s memory bandwidth, 96GB base memory, and nearly identical single-core performance. The extra ¥9750 primarily purchases three things — 16 additional GPU cores (+25%), 6 additional CPU cores (+20%), and the exclusive ability to configure 512GB of unified memory. For most users, the most valuable part of that premium is the 512GB memory gateway.

Spec Comparison: Where the Money Goes

Core spec comparison: aside from memory ceiling and core counts, both are identical in bandwidth, base memory, and neural engine

GPU: 64 → 80 cores (+25% cores). This is visible compute uplift, but note — it's GPU compute, not memory bandwidth.

CPU: 30 → 36 cores (+20%), shifting from 10 performance + 20 efficiency cores to 12 + 24.

Neural Engine: 32 cores on both , no difference.

Base memory: 96GB on both .

Memory bandwidth: 1.2TB/s on both — the most critical point, explained below.

Max memory: 256GB → 512GB — the true dividing line, and 512GB is only available on the 80-core model.

Mainland launch price: ¥46,999 → ¥56,749 , a ¥9,750 difference.

Benchmark Results: 6–8% Multi-core Gain, Single-core Unchanged

Mainstream benchmark comparison: single-core <1%, multi-core +6%~8%, GPU cores +25% (theoretical)

Early M5 Ultra samples (pre-release units, for reference only) show:

Geekbench 6 single-core: 4688 vs 4710 — <1% difference; daily app responsiveness feels identical.

Geekbench 6 multi-core: 39,193 vs 42,327 — ~8% gain.

Cinebench 2024 multi-core: 3701 vs 3905 — ~6% gain.

GPU cores: 64 vs 80 — theoretical graphics compute +25%.

Bottom line: The CPU side yields only 6–8% multi-core improvement for an extra ¥9,750 , a gap barely noticeable in code compilation or batch processing. The real differentiators are the 25% GPU compute uplift and the 512GB memory ceiling.

Biggest Misconception: 80 Cores Won't Make Local LLMs Faster

Many buyers upgrade to 80 cores expecting faster local large-model inference — but LLM token generation speed (decoding throughput) is bound by memory bandwidth, not GPU core count . Since both variants share 1.2TB/s bandwidth, token generation speed is virtually identical when running the same model in the same memory configuration. The extra 16 GPU cores do not accelerate "token emission."

The 80-core GPU compute does accelerate compute-intensive workloads:

3D rendering / Blender : rendering is a classic GPU compute load; 80 cores render ~25% faster.

Video export / effects : M5 Ultra's media engine doubles, but effects compositing and color grading still rely on GPU.

Local image generation (Stable Diffusion / diffusion models) : generation speed scales directly with GPU compute.

LLM prompt processing (prefill) : Apple claims peak AI compute is 4.3× M3 Ultra, thanks to neural accelerators inside each GPU core. This helps with long-context ingestion and long prompt handling — a weakness of the previous Ultra generation.

Summary: 80 cores let you "compute faster," but not "emit tokens faster."

Real Differentiator: 512GB Memory Only on 80-core

The 64-core model maxes out at 256GB ; 512GB requires the 80-core version. This matters because memory capacity = maximum model size you can fit for local LLMs.

70B Q4 ~40GB — fits comfortably in 256GB.

DeepSeek-R1 671B (full precision) quantized to the limit still needs 200GB+.

Only a 512GB machine can hold the entire 671B model in memory without disk swapping , maintaining usable speed.

If you research local LLMs, run 400B+ models, or need massive context windows, the 80-core version isn't "better" — it's the only viable choice . A large portion of the ¥9,750 premium is essentially the admission ticket for 512GB memory.

Who Should Buy 80-core, Who Should Save the ¥9,750

Must buy 80-core : need 512GB memory (400B+ models / huge context / multi-model co-residency); or heavily depend on GPU rendering, video effects, local image generation, and can fully utilize the +25% compute.

64-core is enough : memory needs ≤256GB; primary workload is LLM decoding (speed identical); daily programming, editing, office work — save the ¥9,750 for extra RAM or a better display.

One piece of advice : if you're ordering 80-core just because "it sounds more top-tier," you're likely paying for compute you'll never use. Check your actual memory requirement first, then decide.

Final Thoughts

Back to the original question: what does the extra ¥9,750 on M5 Ultra actually add? Answer: 16 GPU cores, 6 CPU cores, and the sole path to 512GB memory . It's essential for super-large-model or heavy GPU creative work; for those merely wanting "faster," it's a beautiful misunderstanding — because decoding speed depends on bandwidth, and bandwidth is identical on both models.