Radeon Pro W7900 48GBBudget pick
864 GB/s (+464)
B
Raises estimated decode speed by about 112%.26.7 tok/s decode
Raises estimated decode speed by about 112%.
~$3,999 MSRP
Yi 1.5 34B needs ~32.2 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~12 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
12.6 tok/s
TTFT
15409 ms
Safe context
4K
Memory
32.2 GB / 46.1 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 | 11.6 tok/s | 9126 ms | 4K |
| Coding | B | Runs well | 11.6 tok/s | 16730 ms | 4K |
| Agentic Coding | B | Runs well | 11.6 tok/s | 24335 ms | 4K |
| Reasoning | B | Runs well | 11.6 tok/s | 19772 ms | 4K |
| RAG | B | Runs well | 11.6 tok/s | 30419 ms | 4K |
How Yi 1.5 34B (34B params) fits at each quantization level on MacBook Pro M3 Max 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 |
Copy-paste commands to run Yi 1.5 34B on your machine.
Run
lms load Yi-1.5-34B-Chat && lms server startUpgrade options
Raises estimated decode speed by about 112%.
~$3,999 MSRP
Raises estimated decode speed by about 112%.
~$3,999 MSRP
Yes, MacBook Pro M3 Max 64GB can run Yi 1.5 34B with a B grade (Runs well). Expected decode speed: 11.6 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 MacBook Pro M3 Max 64GB, Yi 1.5 34B achieves approximately 11.6 tokens per second decode speed with a time-to-first-token of 16730ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 34B on MacBook Pro M3 Max 64GB receives a B grade with 11.6 tok/s and 4K context.
On MacBook Pro M3 Max 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/yi-1.5-34b-on-m3-max-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
Not always. MacBook Pro M3 Max 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.