RX 7700 XT 12GBBudget pick
432 GB/s (+232)
B
Raises estimated decode speed by about 85%.65.9 tok/s decode
Raises estimated decode speed by about 85%.
~$449 MSRP
Qwen 2.5 Math 7B needs ~7.8 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~36 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
35.6 tok/s
TTFT
5439 ms
Safe context
4K
Memory
7.8 GB / 11.5 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 | 35.6 tok/s | 2967 ms | 4K |
| Coding | B | Runs well | 35.6 tok/s | 5439 ms | 4K |
| Agentic Coding | B | Runs well | 35.6 tok/s | 7911 ms | 4K |
| Reasoning | B | Runs well | 35.6 tok/s | 6427 ms | 4K |
| RAG | B | Runs well | 35.6 tok/s | 9888 ms | 4K |
How Qwen 2.5 Math 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C54 |
Q3_K_S | 3 | 3.4 GB | Low | C54 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 2.5 Math 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "Qwen/Qwen2.5-Math-7B-Instruct" \
--hf-file "Qwen2.5-Math-7B-Instruct-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Raises estimated decode speed by about 85%.
~$449 MSRP
RTX 5070 12GBBest valueRaises estimated decode speed by about 202%.
~$549 MSRP
Yes, MacBook Pro M2 Pro 16GB can run Qwen 2.5 Math 7B with a B grade (Runs well). Expected decode speed: 35.6 tok/s.
Qwen 2.5 Math 7B (7B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 Math 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, Qwen 2.5 Math 7B achieves approximately 35.6 tokens per second decode speed with a time-to-first-token of 5439ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 Math 7B on MacBook Pro M2 Pro 16GB receives a B grade with 35.6 tok/s and 4K context.
On MacBook Pro M2 Pro 16GB, Qwen 2.5 Math 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/qwen-2.5-math-7b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| B55 |
Q4_K_M | 4 | 4.3 GB | Medium | B56 |
Q5_K_M | 5 | 5.0 GB | High | B57 |
Q6_K | 6 | 5.7 GB | High | B57 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B56 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Not always. MacBook Pro M2 Pro 16GB 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.