Mixtral 8x22B needs ~92.9 GB but MacBook Pro M4 Pro 24GB only has 17.3 GB. Try a smaller quantization or lighter model.
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
75.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.3 tok/s
TTFT
84702 ms
Safe context
4K
Memory
92.9 GB / 17.3 GB
Offload
80%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 92.9 GB, but this setup only exposes 17.3 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.3 tok/s | 46201 ms | 4K |
| Coding | F | Too heavy | 2.3 tok/s | 84702 ms | 4K |
| Agentic Coding | F | Too heavy | 2.3 tok/s | 123203 ms | 4K |
| Reasoning | F | Too heavy | 2.3 tok/s | 100103 ms | 4K |
| RAG | F | Too heavy | 2.3 tok/s | 154004 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Mixtral 8x22B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~14 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 13.5 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 9.2 | Heavy offload |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 8.7 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 6.8 | Heavy offload |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 5.0 | Too big |
 RTX 5090 32GB | 32 GB | Q4_K_M | 3.9 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 3.7 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 3.4 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.9 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.6 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 2.6 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.5 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.5 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.4 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.3 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.1 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Mixtral 8x22B (141B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 55.0 GB | Low | F0 |
Q3_K_S | 3 | 69.1 GB | Low | F0 |
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 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 109%.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 300%.
~$3,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$6,999 MSRP
NVIDIA H200 141GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$30,000 MSRP
No, Mixtral 8x22B requires more memory than MacBook Pro M4 Pro 24GB provides.
Mixtral 8x22B (141B parameters) requires approximately 92.9 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 MacBook Pro M4 Pro 24GB, Mixtral 8x22B achieves approximately 2.3 tokens per second decode speed with a time-to-first-token of 84702ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x22B on MacBook Pro M4 Pro 24GB receives a F grade with 2.3 tok/s and 4K context.
On MacBook Pro M4 Pro 24GB, 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.
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. MacBook Pro M4 Pro 24GB 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.
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