Can Nemotron 70B run on Mac Studio M2 Ultra 128GB?
YES — Runs Great
A71Great○Estimated from fit model
Nemotron 70B needs ~62.3 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~11 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 62.3 GB, 11.8 tok/s, Runs well
62.3 GB required92.2 GB available
Memory breakdown
Weights42.7 GB
KV Cache4.9 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsNemotron 70B on Mac Studio M2 Ultra 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 11.8 tok/s decode · 16.4s TTFT (warm) · 30 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|
| Chat | A | Runs well | 10.9 tok/s | 9718 ms | 114K |
| Coding | A | Runs well | 10.9 tok/s | 17816 ms | 114K |
| Agentic Coding | A | Runs well | 10.9 tok/s | 25914 ms | 114K |
| Reasoning | A | Runs well | 10.9 tok/s | 21056 ms | 114K |
| RAG | A | Runs well | 10.9 tok/s | 32393 ms | 114K |
Quantization options
How Nemotron 70B (70B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|
Q2_K | 2 | 27.3 GB | Low | B64 |
Q3_K_S | 3 | 34.3 GB | Low | B66 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Nemotron 70B on your machine.
Your hardware
More models your Mac Studio M2 Ultra 128GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|
👁 Mistral
Devstral 2 123B Instruct | 123B | S | 6.3 tok/s |
👁 Alibaba
Qwen 3.5 122B A10B | 122B | S |
Frequently asked questions
Q8_0Best for your GPU | 8 | 74.9 GB | Very High | B69 |
Not always. Mac Studio M2 Ultra 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
