Qwen 2.5 Math 72B needs ~60.1 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~6 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
Tight fit
Decode
5.7 tok/s
TTFT
33702 ms
Safe context
4K
Memory
60.1 GB / 69.1 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 5.7 tok/s | 18383 ms | 4K |
| Coding | B | Tight fit | 5.7 tok/s | 33702 ms | 4K |
| Agentic Coding | B | Tight fit | 5.7 tok/s | 49020 ms | 4K |
| Reasoning | B | Tight fit | 5.7 tok/s | 39829 ms | 4K |
| RAG | B | Tight fit | 5.7 tok/s | 61276 ms | 4K |
How Qwen 2.5 Math 72B (72B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | B59 |
Q3_K_S | 3 | 35.3 GB | Low | B61 |
NVFP4 | 4 | 40.3 GB | Medium | B61 |
Q4_K_M | 4 | 43.9 GB | Medium | B61 |
Q5_K_MBest for your GPU | 5 | 51.8 GB | High | B61 |
Q6_K | 6 | 59.0 GB | High | F0 |
Q8_0 | 8 | 77.0 GB | Very High | F0 |
F16 | 16 | 147.6 GB | Maximum | F0 |
Copy-paste commands to run Qwen 2.5 Math 72B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "Qwen/Qwen2.5-Math-72B-Instruct" \
--hf-file "Qwen2.5-Math-72B-Instruct-Q4_K_M.gguf" \
-c 4096 -ngl 99Opciones de mejora
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Sube la velocidad estimada de decodificación alrededor de un 102%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
Sube la velocidad estimada de decodificación alrededor de un 91%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
NVIDIA H100 80GBMayor saltoSube la velocidad estimada de decodificación alrededor de un 1123%.
Saca la carga de trabajo de la memoria compartida y la lleva a memoria dedicada del acelerador.
~$40,000 MSRP
Yes, MacBook Pro M2 Max 96GB can run Qwen 2.5 Math 72B with a B grade (Tight fit). Expected decode speed: 5.7 tok/s.
Qwen 2.5 Math 72B (72B parameters) requires approximately 60.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 Math 72B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Max 96GB, Qwen 2.5 Math 72B achieves approximately 5.7 tokens per second decode speed with a time-to-first-token of 33702ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 Math 72B on MacBook Pro M2 Max 96GB receives a B grade with 5.7 tok/s and 4K context.
On MacBook Pro M2 Max 96GB, Qwen 2.5 Math 72B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. MacBook Pro M2 Max 96GB 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/qwen-2.5-math-72b-on-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: