Qwen 2.5 Math 7B needs ~19.8 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~61 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
61.0 tok/s
TTFT
3173 ms
Safe context
4K
Memory
19.8 GB / 92.2 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 | C | Runs well | 61.0 tok/s | 1730 ms | 4K |
| Coding | C | Runs well | 61.0 tok/s | 3173 ms | 4K |
| Agentic Coding | C | Runs well | 61.0 tok/s | 4615 ms | 4K |
| Reasoning | C | Runs well | 61.0 tok/s | 3749 ms | 4K |
| RAG | C | Runs well | 61.0 tok/s | 5768 ms | 4K |
How Qwen 2.5 Math 7B (7B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C43 |
Q3_K_S | 3 | 3.4 GB | Low | C43 |
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 61%.
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
RTX PRO 6000 Blackwell Workstation Edition 96GBBest valueRaises estimated decode speed by about 61%.
~$9,999 MSRP
Yes, MacBook Pro M3 Max 128GB can run Qwen 2.5 Math 7B with a C grade (Runs well). Expected decode speed: 61.0 tok/s.
Qwen 2.5 Math 7B (7B parameters) requires approximately 19.8 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 M3 Max 128GB, Qwen 2.5 Math 7B achieves approximately 61.0 tokens per second decode speed with a time-to-first-token of 3173ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 Math 7B on MacBook Pro M3 Max 128GB receives a C grade with 61.0 tok/s and 4K context.
On MacBook Pro M3 Max 128GB, 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-m3-max-128gb" 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 |
| C43 |
Q4_K_M | 4 | 4.3 GB | Medium | C43 |
Q5_K_M | 5 | 5.0 GB | High | C43 |
Q6_K | 6 | 5.7 GB | High | C43 |
Q8_0 | 8 | 7.5 GB | Very High | C44 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C44 |
Not always. MacBook Pro M3 Max 128GB 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.