Yi 1.5 34B needs ~32.2 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~4 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
8.1 tok/s
TTFT
23780 ms
Safe context
4K
Memory
32.2 GB / 46.1 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 4.2 tok/s | 25349 ms | 4K |
| Coding | B | Runs well | 4.2 tok/s | 46473 ms | 4K |
| Agentic Coding | B | Runs well | 4.2 tok/s | 67597 ms | 4K |
| Reasoning | B | Runs well | 4.2 tok/s | 54923 ms | 4K |
| RAG | B | Runs well | 4.2 tok/s | 84496 ms | 4K |
How Yi 1.5 34B (34B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | B57 |
Q3_K_S | 3 | 16.7 GB | Low | B58 |
NVFP4 | 4 | 19.0 GB | Medium | B59 |
Q4_K_M | 4 | 20.7 GB | Medium | B60 |
Q5_K_M | 5 | 24.5 GB | High | B61 |
Q6_K | 6 | 27.9 GB | High | B61 |
Q8_0Best for your GPU | 8 | 36.4 GB | Very High | B60 |
F16 | 16 | 69.7 GB | Maximum | F0 |
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 288%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 260%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 64GB can run Yi 1.5 34B with a B grade (Runs well). Expected decode speed: 4.2 tok/s.
Yi 1.5 34B (34B parameters) requires approximately 32.2 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 mini M4 64GB, Yi 1.5 34B achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 46473ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 34B on Mac mini M4 64GB receives a B grade with 4.2 tok/s and 4K context.
On Mac mini M4 64GB, 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.
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. Mac mini M4 64GB 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-m4-mini-64gb" 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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