Mixtral 8x7B needs ~36.7 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~14 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
2.1 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~1.7 GB host RAM)
Decode
13.6 tok/s
TTFT
14277 ms
Safe context
4K
Memory
36.7 GB / 34.6 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 1.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~0.9 GB host RAM) | 14.1 tok/s | 7469 ms | 4K |
| Coding | C | Runs with offload (needs ~1.7 GB host RAM) | 13.6 tok/s | 14277 ms | 4K |
| Agentic Coding | C | Very compromised (needs ~3 GB host RAM) | 12.6 tok/s | 22334 ms | 4K |
| Reasoning | C | Runs with offload (needs ~1.7 GB host RAM) | 13.6 tok/s | 16873 ms | 4K |
| RAG | C | Very compromised (needs ~3 GB host RAM) | 12.6 tok/s | 27917 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Mixtral 8x7B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~85 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? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 84.5 | Fits |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 77.1 | Fits |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 66.0 | Fits |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 58.1 | Fits |
| RTX 5090 32GB | 32 GB | Q4_K_M | 54.9 | Heavy offload |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 40.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 33.4 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 31.6 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 24.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 24.7 | Tight |
| RTX 4090 24GB | 24 GB | Q4_K_M | 19.6 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 18.0 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 17.3 | Tight |
| RTX 3090 24GB | 24 GB | Q4_K_M | 16.7 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 15.8 | Tight |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 13.6 | Offloads |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 7.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 4.1 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.6 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.1 | 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 8x7B (47B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.3 GB | Low | B65 |
Q3_K_S | 3 | 23.0 GB | Low | B64 |
NVFP4Best for your GPU | 4 | 26.3 GB | Medium | B64 |
Q4_K_M | 4 | 28.7 GB | Medium | F0 |
Q5_K_M | 5 | 33.8 GB | High | F0 |
Q6_K | 6 | 38.5 GB | High | F0 |
Q8_0 | 8 | 50.3 GB | Very High | F0 |
F16 | 16 | 96.4 GB | Maximum | F0 |
Copy-paste commands to run Mixtral 8x7B on your machine.
Run
ollama run mixtralOpciones de mejora
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,099 MSRP
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,599 MSRP
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 82%.
~$2,499 MSRP
RTX PRO 5000 Blackwell 48GBMayor saltoElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 497%.
~$4,999 MSRP
Yes, MacBook Pro M4 Pro 48GB can run Mixtral 8x7B with a C grade (Runs with offload (needs ~1.7 GB host RAM)). Expected decode speed: 13.6 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 36.7 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 MacBook Pro M4 Pro 48GB, Mixtral 8x7B achieves approximately 13.6 tokens per second decode speed with a time-to-first-token of 14277ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on MacBook Pro M4 Pro 48GB receives a C grade with 13.6 tok/s and 4K context.
On MacBook Pro M4 Pro 48GB, Mixtral 8x7B can safely use up to 4K tokens of context. The model's official context limit is 33K, 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. MacBook Pro M4 Pro 48GB 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-8x7b-on-m4-pro-48gb" 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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