Mixtral 8x22B needs ~104.2 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~9 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
12.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~9.9 GB host RAM)
Decode
8.7 tok/s
TTFT
22137 ms
Safe context
4K
Memory
104.2 GB / 92.2 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 9.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Very compromised (needs ~8.6 GB host RAM) | 8.9 tok/s | 11810 ms | 4K |
| Coding | C | Very compromised (needs ~9.9 GB host RAM) | 8.7 tok/s | 22137 ms | 4K |
| Agentic Coding | C | Very compromised (needs ~12.3 GB host RAM) | 8.4 tok/s | 33583 ms | 4K |
| Reasoning | C | Very compromised (needs ~9.9 GB host RAM) | 8.7 tok/s | 26162 ms | 4K |
| RAG | C | Very compromised (needs ~12.3 GB host RAM) | 8.4 tok/s | 41979 ms | 4K |
How Mixtral 8x22B (141B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 55.0 GB | Low | B61 |
Q3_K_SBest for your GPU | 3 | 69.1 GB | Low | B61 |
NVFP4 | 4 | 79.0 GB | Medium | F0 |
Q4_K_M | 4 | 86.0 GB | Medium | F0 |
Q5_K_M | 5 | 101.5 GB | High | F0 |
Q6_K | 6 | 115.6 GB | High | F0 |
Q8_0 | 8 | 150.9 GB | Very High | F0 |
F16 | 16 | 289.0 GB | Maximum | F0 |
Copy-paste commands to run Mixtral 8x22B on your machine.
Run
ollama run mixtral:8x22bアップグレードオプション
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 55%.
〜$6,999 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 1522%.
〜$8,000 MSRP
NVIDIA H200 141GB最大の飛躍Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 1020%.
〜$30,000 MSRP
Yes, Mac Studio M1 Ultra 128GB can run Mixtral 8x22B with a C grade (Very compromised (needs ~9.9 GB host RAM)). Expected decode speed: 8.7 tok/s.
Mixtral 8x22B (141B parameters) requires approximately 104.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Mixtral 8x22B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M1 Ultra 128GB, Mixtral 8x22B achieves approximately 8.7 tokens per second decode speed with a time-to-first-token of 22137ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x22B on Mac Studio M1 Ultra 128GB receives a C grade with 8.7 tok/s and 4K context.
On Mac Studio M1 Ultra 128GB, Mixtral 8x22B can safely use up to 4K tokens of context. The model's official context limit is 66K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x22b-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>
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