RTX PRO 5000 Blackwell 48GBBudget pick1344 GB/s (+1071)
B
Raises estimated decode speed by about 198%.59.1 tok/s decode
Raises estimated decode speed by about 198%.
~$4,999 MSRP
Yi 34B Chat needs ~32.2 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~20 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
19.8 tok/s
TTFT
9774 ms
Safe context
77K
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 | C | Runs well | 19.8 tok/s | 5331 ms | 77K |
| Coding | C | Runs well | 19.8 tok/s | 9774 ms | 77K |
| Agentic Coding | C | Runs well | 19.8 tok/s | 14217 ms | 77K |
| Reasoning | C | Runs well | 19.8 tok/s | 11551 ms | 77K |
| RAG | C | Runs well | 19.8 tok/s | 17771 ms | 77K |
How Yi 34B Chat (34B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | C46 |
Q3_K_S | 3 | 16.7 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run Yi 34B Chat on your machine.
Run
lms load Yi-34B-Chat && lms server startUpgrade options
RTX PRO 5000 Blackwell 48GBBudget pickRaises estimated decode speed by about 198%.
~$4,999 MSRP
RTX 6000 Ada 48GBBest valueRaises estimated decode speed by about 108%.
~$6,800 MSRP
Yes, MacBook Pro M4 Pro 64GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 19.8 tok/s.
Yi 34B Chat (34B parameters) requires approximately 32.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 34B Chat is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 64GB, Yi 34B Chat achieves approximately 19.8 tokens per second decode speed with a time-to-first-token of 9774ms using Q4_K_M quantization.
For coding workloads, Yi 34B Chat on MacBook Pro M4 Pro 64GB receives a C grade with 19.8 tok/s and 77K context.
On MacBook Pro M4 Pro 64GB, Yi 34B Chat can safely use up to 77K tokens of context. The model's official context limit is 200K, 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-34b-chat-on-m4-pro-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 |
| C48 |
Q4_K_M | 4 | 20.7 GB | Medium | C48 |
Q5_K_M | 5 | 24.5 GB | High | C50 |
Q6_K | 6 | 27.9 GB | High | C50 |
Q8_0Best for your GPU | 8 | 36.4 GB | Very High | C49 |
F16 | 16 | 69.7 GB | Maximum | F0 |
Not always. MacBook Pro M4 Pro 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.