Is Apple's Unified Memory More Valuable Than NVIDIA's Raw GPU Power?

It depends on the job. Neither spec wins outright. For running large language models, memory comes first: if the model doesn't fit in memory the GPU can use, nothing else on the spec sheet matters, and Apple's Mac Studio offers up to 512 GB of unified memory. Once the model fits, memory bandwidth sets how fast it writes answers. For long prompts, image generation and training, raw GPU compute takes over, and NVIDIA's discrete GPUs are measurably faster there. The common mistake is to compare TOPS or teraflops figures and ignore the other two numbers.

Which specification actually predicts AI performance?

Source: Apple, NVIDIA, ASUS and AMD specifications; MacStories (Sep 2026); Hardware Corner (2026); NVIDIA GB10 and Ryzen AI Max+ 395 community benchmarks compiled from published sources. The ceiling column is calculated as bandwidth divided by about 40 GB, the approximate size of a 70B model at 4-bit, and is an upper bound, not a measurement. Measured results use different models, engines and quantisations, so compare them within a row, not across rows.

Why does memory capacity come first?

A language model has to sit in memory the GPU can reach. A 70B-parameter model at 4-bit needs about 40 GB before you add room for the conversation (the KV cache), and a 120B model needs more. On an RTX 5090 with 32 GB, a 70B model simply doesn't fit. Part of it ends up in system RAM, and MacStories measured the RTX 5090 PC falling to between 1.5 and 4.6 tokens per second when that happened.

This is where unified memory changes the comparison. On a Mac Studio, the CPU and GPU share one pool, and the M5 Ultra offers 256 GB or 512 GB. There are two caveats buyers often miss:

• Not all of it goes to the GPU. macOS lets the GPU use about 75% of unified memory by default, so a 256 GB Mac Studio gives the GPU about 192 GB. That can be raised with a terminal command Apple doesn't support. MacStories saw macOS stop a model server under memory pressure at 176.6 GB on a 256 GB machine.
• Other unified-memory systems have limits too. RTX Spark's 128 GB models give the GPU about 64 GB by default (laptop data), and AMD Ryzen AI Max+ systems about 96 GB on Windows.

So the useful number is GPU-usable memory, not the headline total. Our post on why 128 GB of unified memory matters and our look at whether 128 GB is still enough cover sizing in more detail.

Why is memory bandwidth the speed limit for chat?

When a model writes an answer, it reads all of its active weights from memory for every token. That makes token generation ("decode") limited mainly by memory bandwidth, not by compute. A rough upper bound is bandwidth divided by model size. For a 40 GB model, that's about 31 tokens per second on an M5 Ultra and about 7 on a 273 GB/s GB10. Real results come in lower: the GB10 ran Llama 3.1 70B at 4.4 tokens per second in Ollama, about 65% of its ceiling.

Two points follow. First, two systems with the same memory capacity can differ several-fold in chat speed. Second, mixture-of-experts models such as gpt-oss-120b only read a fraction of their weights per token, so they generate much faster than their total size suggests. That's why a 120B model can be quicker than a 70B dense model on the same machine.

Capacity decides what you can run. Bandwidth decides how fast it answers. Compute decides how fast it reads your prompt.

When does raw GPU compute matter more?

Before a model can answer, it must process the whole prompt ("prefill"). That step is compute-bound, and it dominates when you paste in long documents, run retrieval over many pages, or build an agent that resends a long context on every step. NVIDIA's tensor cores show a clear lead here in published tests:

• On gpt-oss-120b with a 4K prompt, Hardware Corner measured an RTX PRO 6000 at 6,512 prompt tokens per second against 1,325 for an M5 Max MacBook Pro.
• With a 63K-token prompt, MacStories measured 26.4 seconds to the first token on the RTX 5090 against 42.8 seconds on the M5 Ultra.

Compute also dominates image and video generation, fine-tuning and training. Apple has narrowed the gap. Its M5 Ultra claims are "up to 4.3x the peak AI compute" of the M3 Ultra and up to 4x faster prompt processing in LM Studio. Neither is a token-generation claim. Our post on whether unified memory replaces high-VRAM GPUs looks at this trade-off by workload.

What do spec sheets leave out?

• Software. A fast GPU is only useful if your tools support it. Mac Studio has no CUDA, while RTX Spark's CUDA on Windows on Arm is a developer preview. See our post on CUDA versus Apple's MLX.
• Concurrency. Serving many users at once depends on batching software as much as hardware. NVIDIA's vLLM stack is more mature than Apple's new vllm-metal plugin.
• Power and noise. Apple rates the M5 Ultra Mac Studio at 385 W maximum. An RTX 5090 card alone is rated at 575 W.
• Like-for-like testing. Many published comparisons use different engines or quantisations on each side. Tom's Hardware has the M5 Ultra ahead of a DGX Spark on both prefill and generation, but not on the same engine.

How should you read a spec sheet before buying?

1. List your largest model and context length, then work out the memory it needs, including KV cache.
2. Check GPU-usable memory, not total memory, and leave headroom.
3. Compare bandwidth if most of your use is chatting or generating text.
4. Compare compute and look for measured prefill results if you process long documents, generate images or fine-tune.
5. Confirm your software stack runs on the platform before comparing anything else.

Our Mac Studio versus GB10 guide for 70B models and our explainer on NVIDIA's 120B and 200B model claims apply this to specific models.

Buying in Singapore

SourceIT supplies both unified-memory and discrete-GPU systems. Mac Studio models from the M5 Max with 128 GB to the M5 Ultra with 512 GB are price on request; for reference, the 128 GB/1 TB M5 Max is about S$7,849 configured (calculated from Apple Singapore's upgrade pricing — confirm on apple.com/sg). For a 128 GB NVIDIA GB10 system, the ASUS Ascent GX10 128GB/1TB is S$10,350 and the NVIDIA DGX Spark 4TB is S$9,300 (listed 8 Nov 2026). For 96 GB of GPU memory at 1,792 GB/s, the RTX PRO 6000 Blackwell Max-Q is S$29,100. RTX Spark options include the ASUS ProArt GR1X, price on request. Prices change; ask for a current quotation. Use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.

Frequently asked questions

Is unified memory as fast as GPU VRAM? Not usually. An M5 Ultra's unified memory runs at 1.2 TB/s, against 1,792 GB/s on an RTX 5090 or RTX PRO 6000. Its advantage is capacity: up to 512 GB in one pool, against 32 GB or 96 GB on those cards.

How much of a Mac Studio's memory can the GPU use? About 75% by default, so roughly 96 GB on a 128 GB machine and 192 GB on a 256 GB machine. It can be raised with an unsupported terminal setting, but macOS still needs memory for itself.

Do TOPS figures tell me how fast a chatbot will run? No. TOPS measures peak compute, which mainly affects prompt processing. Chat speed depends mostly on memory bandwidth and whether the model fits in GPU-usable memory.

Why is my RTX 5090 slow on a 70B model? At 4-bit, a 70B model needs about 40 GB, more than the card's 32 GB. The overflow runs from system memory, which is far slower, and published tests show speeds falling to a few tokens per second.

Which matters more for document search and AI agents? Both bandwidth and compute. Agents and retrieval send long prompts repeatedly, so prefill speed matters, which favours NVIDIA. Large models with long contexts also need memory capacity, which favours the larger Mac Studio configurations.