Raises estimated decode speed by about 224%.
~$9,999 MSRP
Yi 1.5 34B needs ~39.1 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~24 tok/s.
Operating mode
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
Fit status
Runs well
Decode
24.3 tok/s
TTFT
7970 ms
Safe context
4K
Memory
39.1 GB / 92.2 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 24.3 tok/s | 4347 ms | 4K |
| Coding | B | Runs well | 24.3 tok/s | 7970 ms | 4K |
| Agentic Coding | B | Runs well | 24.3 tok/s | 11593 ms | 4K |
| Reasoning | B | Runs well | 24.3 tok/s | 9420 ms | 4K |
| RAG | B | Runs well | 24.3 tok/s | 14492 ms | 4K |
How Yi 1.5 34B (34B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | C53 |
Q3_K_S | 3 | 16.7 GB | Low | C53 |
NVFP4 | 4 | 19.0 GB | Medium | C53 |
Q4_K_M | 4 | 20.7 GB | Medium | C54 |
Q5_K_M | 5 | 24.5 GB | High | C54 |
Q6_K | 6 | 27.9 GB | High | C55 |
Q8_0 | 8 | 36.4 GB | Very High | B57 |
F16Best for your GPU | 16 | 69.7 GB | Maximum | B60 |
Copy-paste commands to run Yi 1.5 34B on your machine.
Run
lms load Yi-1.5-34B-Chat && lms server startOpções de upgrade
Raises estimated decode speed by about 224%.
~$9,999 MSRP
Raises estimated decode speed by about 189%.
~$9,999 MSRP
Yes, Mac Studio M2 Ultra 128GB can run Yi 1.5 34B with a B grade (Runs well). Expected decode speed: 24.3 tok/s.
Yi 1.5 34B (34B parameters) requires approximately 39.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 34B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M2 Ultra 128GB, Yi 1.5 34B achieves approximately 24.3 tokens per second decode speed with a time-to-first-token of 7970ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 34B on Mac Studio M2 Ultra 128GB receives a B grade with 24.3 tok/s and 4K context.
On Mac Studio M2 Ultra 128GB, Yi 1.5 34B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/yi-1.5-34b-on-m2-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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