Raises estimated decode speed by about 532%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$9,999 MSRP
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VOOZH | about |
Qwen 2.5 Math 72B needs ~63.5 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 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
Runs well
Decode
5.9 tok/s
TTFT
32578 ms
Safe context
4K
Memory
63.5 GB / 92.2 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 | Runs well | 5.9 tok/s | 17770 ms | 4K |
| Coding | B | Runs well | 5.9 tok/s | 32578 ms | 4K |
| Agentic Coding | B | Runs well | 5.9 tok/s | 47386 ms | 4K |
| Reasoning | B | Runs well | 5.9 tok/s | 38502 ms | 4K |
| RAG | B | Runs well | 5.9 tok/s | 59233 ms | 4K |
How Qwen 2.5 Math 72B (72B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | B56 |
Q3_K_S | 3 | 35.3 GB | Low | B58 |
NVFP4 | 4 | 40.3 GB | Medium | B59 |
Q4_K_M | 4 | 43.9 GB | Medium | B60 |
Q5_K_M | 5 | 51.8 GB | High | B61 |
Q6_K | 6 | 59.0 GB | High | B61 |
Q8_0Best for your GPU | 8 | 77.0 GB | Very High | B61 |
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 99Upgrade options
Raises estimated decode speed by about 532%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$9,999 MSRP
Raises estimated decode speed by about 463%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$9,999 MSRP