RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick1792 GB/s (+992)
B
Raises estimated decode speed by about 224%.108.3 tok/s decode
Raises estimated decode speed by about 224%.
~$9,999 MSRP
Mixtral 8x7B needs ~45.3 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~31 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
33.4 tok/s
TTFT
5801 ms
Safe context
33K
Memory
45.3 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 | 33.4 tok/s | 3164 ms | 33K |
| Coding | B | Runs well | 31.0 tok/s | 6236 ms | 33K |
| Agentic Coding | B | Runs well | 33.4 tok/s | 8437 ms | 33K |
| Reasoning | B | Runs well | 33.4 tok/s | 6855 ms | 33K |
| RAG | B | Runs well | 33.4 tok/s | 10547 ms | 33K |
How Mixtral 8x7B (47B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.3 GB | Low | B56 |
Q3_K_S | 3 | 23.0 GB | Low | B57 |
NVFP4 | 4 |
Copy-paste commands to run Mixtral 8x7B on your machine.
Run
ollama run mixtralUpgrade options
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickRaises estimated decode speed by about 224%.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 189%.
~$9,999 MSRP
Yes, Mac Studio M2 Ultra 128GB can run Mixtral 8x7B with a B grade (Runs well). Expected decode speed: 31.0 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 45.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Mixtral 8x7B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M2 Ultra 128GB, Mixtral 8x7B achieves approximately 31.0 tokens per second decode speed with a time-to-first-token of 6236ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on Mac Studio M2 Ultra 128GB receives a B grade with 31.0 tok/s and 33K context.
On Mac Studio M2 Ultra 128GB, Mixtral 8x7B can safely use up to 33K tokens of context. The model's official context limit is 33K, 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/mixtral-8x7b-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>
Preview:
26.3 GB |
| Medium |
| B57 |
Q4_K_M | 4 | 28.7 GB | Medium | B58 |
Q5_K_M | 5 | 33.8 GB | High | B59 |
Q6_K | 6 | 38.5 GB | High | B60 |
Q8_0Best for your GPU | 8 | 50.3 GB | Very High | B63 |
F16 | 16 | 96.4 GB | Maximum | F0 |
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.