WizardMath 7B needs ~8.9 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~35 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
35.2 tok/s
TTFT
5493 ms
Safe context
4K
Memory
8.9 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 | A | Runs well | 35.2 tok/s | 2996 ms | 4K |
| Coding | A | Runs well | 35.2 tok/s | 5493 ms | 4K |
| Agentic Coding | A | Tight fit | 35.2 tok/s | 7990 ms | 4K |
| Reasoning | A | Runs well | 35.2 tok/s | 6492 ms | 4K |
| RAG | A | Tight fit | 35.2 tok/s | 9987 ms | 4K |
How WizardMath 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 | B70 |
Q3_K_S | 3 | 3.4 GB | Low | A71 |
NVFP4 | 4 |
Copy-paste commands to run WizardMath 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "WizardLMTeam/WizardMath-7B-V1.1" \
--hf-file "WizardMath-7B-V1.1-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 27.4 tok/s | |
| Qwen 3 14B | 14B | A | 13.8 tok/s |
Yes, MacBook Pro M2 Pro 16GB can run WizardMath 7B with a A grade (Runs well). Expected decode speed: 35.2 tok/s.
WizardMath 7B (7B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.
The recommended quantization for WizardMath 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, WizardMath 7B achieves approximately 35.2 tokens per second decode speed with a time-to-first-token of 5493ms using Q4_K_M quantization.
For coding workloads, WizardMath 7B on MacBook Pro M2 Pro 16GB receives a A grade with 35.2 tok/s and 4K context.
On MacBook Pro M2 Pro 16GB, WizardMath 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/wizard-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 |
| A72 |
Q4_K_M | 4 | 4.3 GB | Medium | A72 |
Q5_K_M | 5 | 5.0 GB | High | A73 |
Q6_K | 6 | 5.7 GB | High | A73 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A72 |
F16 | 16 | 14.3 GB | Maximum | F0 |
| Qwen 3 8B | 8B | S | 30.8 tok/s |
 Nemotron Nano 8B | 8B | S | 30.8 tok/s |
 Ministral 3 14B | 14B | B | 13.7 tok/s |
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.