RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick1792 GB/s (+992)
C
Raises estimated decode speed by about 243%.77.1 tok/s decode
Raises estimated decode speed by about 243%.
~$9,999 MSRP
baichuan inc Baichuan M2 32B needs ~38.0 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~23 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
22.5 tok/s
TTFT
8589 ms
Safe context
247K
Memory
38.0 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 | C | Runs well | 22.5 tok/s | 4685 ms | 247K |
| Coding | C | Runs well | 22.5 tok/s | 8589 ms | 247K |
| Agentic Coding | C | Runs well | 22.5 tok/s | 12493 ms | 247K |
| Reasoning | C | Runs well | 22.5 tok/s | 10150 ms | 247K |
| RAG | C | Runs well | 22.5 tok/s | 15616 ms | 247K |
How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | D40 |
Q3_K_S | 3 | 15.7 GB | Low | D40 |
NVFP4 | 4 |
Copy-paste commands to run baichuan inc Baichuan M2 32B on your machine.
Run
lms load hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf && lms server startUpgrade options
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickRaises estimated decode speed by about 243%.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 205%.
~$9,999 MSRP
Yes, Mac Studio M1 Ultra 128GB can run baichuan inc Baichuan M2 32B with a C grade (Runs well). Expected decode speed: 22.5 tok/s.
baichuan inc Baichuan M2 32B (32B parameters) requires approximately 38.0 GB of memory with Q4_K_M quantization.
The recommended quantization for baichuan inc Baichuan M2 32B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M1 Ultra 128GB, baichuan inc Baichuan M2 32B achieves approximately 22.5 tokens per second decode speed with a time-to-first-token of 8589ms using Q4_K_M quantization.
For coding workloads, baichuan inc Baichuan M2 32B on Mac Studio M1 Ultra 128GB receives a C grade with 22.5 tok/s and 247K context.
On Mac Studio M1 Ultra 128GB, baichuan inc Baichuan M2 32B can safely use up to 247K tokens of context. The model's official context limit is —, 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/hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf-on-m1-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
17.9 GB |
| Medium |
| C40 |
Q4_K_M | 4 | 19.5 GB | Medium | C40 |
Q5_K_M | 5 | 23.0 GB | High | C41 |
Q6_K | 6 | 26.2 GB | High | C41 |
Q8_0 | 8 | 34.2 GB | Very High | C43 |
F16Best for your GPU | 16 | 65.6 GB | Maximum | C47 |
Not always. Mac Studio M1 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.