For generating text from a dense 70B model such as Llama 3.3 70B, a Mac Studio should be clearly faster than an NVIDIA GB10 system such as DGX Spark. Our caveat is that no one has yet published a measured M5 Ultra result for a 70B model. GB10 measured about 4.4 tokens per second at 4-bit and 2.7 at FP8, which is slow for interactive chat. Memory-bandwidth maths puts the M5 Ultra's ceiling at roughly 29 tokens per second at 4-bit. Our estimate of what it will actually reach is about 15–23, based on how efficiently other machines run and on measured results from smaller models. GB10 still has real strengths for 70B work. It holds an FP8 70B model with long context without any tweaks, it supports NVIDIA's NVFP4 format natively, and NVIDIA has published 70B fine-tuning results. The better machine depends on whether you are chatting with a 70B model, feeding it long documents or training it.
How fast does a 70B model run on each machine?
| Machine (bandwidth) | 70B at 4-bit (~42.5 GB) | 70B at FP8 (~72.7 GB) | Status |
|---|---|---|---|
| NVIDIA GB10 / DGX Spark (273 GB/s) | 4.4 tok/s measured (ceiling 6.4) | 2.7 tok/s measured (ceiling 3.8) | Measured: Ollama and SGLang |
| Mac Studio M5 Max 128 GB (614 GB/s) | Ceiling 14.4 tok/s | Ceiling 8.4 tok/s | Calculated; no published test |
| Mac Studio M5 Ultra (1.2 TB/s) | Ceiling 28.9 tok/s; estimate 15–23 | Ceiling 16.9 tok/s | Calculated; no published test |
| For reference: RTX PRO 6000 (1,792 GB/s) | About 32 tok/s measured (ceiling 42) | Ceiling 24.6 tok/s | Measured at 4-bit |
Source: GB10 4-bit from Ollama v0.12.6 benchmarks of Llama 3.1 70B q4_K_M (23 Oct 2025); GB10 FP8 from LMSYS with SGLang (13 Oct 2025); RTX PRO 6000 from hardware-corner (Llama 3.3 70B Q4_K, 4K context). Model sizes from NVIDIA's and unsloth's Hugging Face files. Ceilings are our calculation: memory bandwidth divided by model size, the theoretical maximum for generation. GB10 reaches about 69% of its ceiling at 4-bit. The M5 Ultra estimate assumes 60–80% of the ceiling (17–23 tok/s), widened to 15 for long contexts, as explained below. Generation speed only; prompt processing is separate.
Why is the Mac faster at writing text?
A dense 70B model reads all of its weights from memory for every token it generates. At 4-bit that is about 42.5 GB per token, so speed is set almost entirely by memory bandwidth. The M5 Ultra's 1.2 TB/s is about 4.5 times GB10's 273 GB/s, and even the M5 Max's 614 GB/s is more than double.
In practice, 4.4 tokens per second on GB10 is workable for batch jobs but sluggish for chat. An M5 Ultra in our 15–23 range would feel responsive. Two checks support the range. Applying 60–80% efficiency to the bandwidth ceiling gives 17–23 tokens per second. NetworkChuck measured 42.6 tokens per second on Qwen3.8 27B at 4-bit on a 256 GB M5 Ultra with a 32K-token prompt; scaled to a model 2.6 times larger, that suggests about 16 at that context length. The M5 Ultra's measured decode on other models is also about 1.5–1.6 times the M3 Ultra, in line with its bandwidth increase. Until someone publishes a 70B test, though, treat 15–23 as an estimate.
Which quantisation should you use?
- 4-bit (Q4_K_M in llama.cpp, 4-bit in MLX): about 42.5 GB. This is the practical choice on both platforms, with the best speed and a modest quality loss.
- NVFP4: about 42.7 GB. NVIDIA's 4-bit format has hardware support on GB10 and runs through TensorRT-LLM and vLLM. MLX can now run NVFP4 models (Apple used NVFP4 in its own cluster tests), but NVIDIA's tooling is more mature for it. We found no published GB10 NVFP4 70B generation figure.
- FP8 / 8-bit: about 72.7 GB. Better quality, but generation is slower on every machine, and memory gets tight on smaller configurations.
- 16-bit: about 141 GB. Doesn't fit on GB10's 128 GB; needs a 256 GB or 512 GB M5 Ultra.
For more on how 70B-plus claims are quoted, see what NVIDIA's 120B and 200B claims really mean.
How much context can each machine hold?
Context memory (the KV cache) is where many 70B setups run out of room. Llama 3.3 70B needs about 0.33 MB per token at 16-bit, so a 32K-token conversation adds about 10.7 GB and a 128K context about 43 GB (our calculation). Combined with the weights:
- 4-bit + 32K: about 53 GB. Fits everywhere, including the 96 GB M5 Ultra, whose default GPU limit is about 72 GB.
- 4-bit + 128K: about 86 GB. Fits GB10 (about 119 GB usable), the 128 GB M5 Max (about 96 GB default) and a 256 GB M5 Ultra. It exceeds the 96 GB Ultra's default limit.
- FP8 + 32K: about 83 GB. Fits GB10, the 128 GB M5 Max and 256 GB Ultra. It won't fit a 96 GB Ultra without raising the GPU limit through an unsupported setting.
- FP8 + 128K: about 116 GB. Only GB10 (just) and a 256 GB or 512 GB M5 Ultra hold this.
Quantising the KV cache to 8-bit roughly halves those context figures, and both llama.cpp and MLX support it. Our guide to why 128 GB of unified memory matters explains the shared-memory basics, and is 128 GB still enough? covers what happens above 70B.
For 70B chat, bandwidth wins and the Mac leads. For 70B training and deployment to NVIDIA servers, software wins and GB10 leads.
What about prompt processing and long documents?
Reading a prompt is compute-bound, not bandwidth-bound, so the gap narrows or reverses. GB10 processed prompts at about 1,911 tokens per second on Llama 3.1 70B at 4-bit (Ollama) and about 803 at FP8 (SGLang), so a 32K-token document would take roughly 17 to 41 seconds before the first word. No 70B prefill figure exists for the M5 Ultra. Scaling NetworkChuck's measured 1,526 tokens per second on the 27B model by model size suggests something like 600 for a 70B, or close to a minute for a 32K prompt. That is a rough estimate of ours, not a measurement. For heavy document question-answering on a dense 70B, GB10 may be the more comfortable machine.
Is a dense 70B even the right model?
Often not. Mixture-of-experts models with similar or better quality read far fewer parameters per token. On gpt-oss-120b, the official llama.cpp benchmark for DGX Spark shows about 59 tokens per second, and a single community run on an M5 Ultra reported about 110 using a different engine. Both are far faster than a dense 70B on the same hardware. If you don't specifically need Llama 3.3 70B or a fine-tune of it, a MoE model makes either machine feel quicker. Our published GB10 vs Mac Studio overview covers the general comparison, and the local AI war looks at the wider ecosystem.
Which is better for fine-tuning a 70B model?
GB10. NVIDIA's DGX Spark page says it can "fine-tune AI models up to 70 billion parameters", and its developer blog table shows QLoRA on Llama 3.3 70B at 760 tokens per second (PyTorch, sequence length 2,048). The CUDA ecosystem, including Unsloth, bitsandbytes and NeMo, is built for this. mlx-lm on the Mac supports LoRA on quantised models, but we found no published 70B fine-tuning throughput for the M5 generation. Our GB10 LoRA and QLoRA guide has the details, and unified memory vs GPU power explains why bandwidth and compute favour different tasks.
Buying in Singapore
The NVIDIA DGX Spark 4 TB is S$9,300 and the ASUS Ascent GX10 128 GB/1TB is S$10,350 at SourceIT (listed 8 Nov 2026; prices change, so ask for a current quotation). Avoid the 64 GB GB10 models for FP8 70B work. On the Apple side, the Mac Studio M5 Ultra 96 GB/1TB is S$7,999 at Apple Singapore's list price. The M5 Max 128 GB/1TB is about S$7,849 and the M5 Ultra 256 GB/2TB about S$16,699, both calculated from Apple Singapore's upgrade pricing (confirm on apple.com/sg). SourceIT lists them as price on request; ask us for a 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
How many tokens per second does DGX Spark get on a 70B model? Published tests show about 4.4 tokens per second at 4-bit (Ollama) and 2.7 at FP8 (SGLang) for Llama 3.1 70B. Speculative decoding and newer software can improve this.
How fast is a Mac Studio M5 Ultra on Llama 3.3 70B? No measured result has been published yet. Its 1.2 TB/s bandwidth sets a ceiling of about 29 tokens per second at 4-bit, and our estimate is about 15–23 in practice, with the lower end at long contexts.
Can the 96 GB Mac Studio M5 Ultra run a 70B model? Yes at 4-bit with moderate context. FP8, or 4-bit with a 128K context, exceeds its default GPU memory limit of about 72 GB, so choose 256 GB for those.
Is the M5 Max 128 GB enough for 70B? Yes for 4-bit, even with a 128K context, and for FP8 with moderate context. Its 614 GB/s bandwidth gives a 4-bit ceiling of about 14 tokens per second, roughly half the Ultra's.
Which should I buy to fine-tune a 70B model? GB10. NVIDIA publishes 70B QLoRA results and playbooks for it, and the CUDA training tools are more mature than the Mac's.
