Raises estimated decode speed by about 146%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP
Qwen 2.5 Math 72B needs ~59.3 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~34 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
37.3 tok/s
TTFT
5194 ms
Safe context
4K
Memory
59.3 GB / 96.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 37.3 tok/s | 2833 ms | 4K |
| Coding | B | Runs well | 34.3 tok/s | 5649 ms | 4K |
| Agentic Coding | B | Runs well | 37.3 tok/s | 7555 ms | 4K |
| Reasoning | B | Runs well | 37.3 tok/s | 6139 ms | 4K |
| RAG | B | Runs well | 37.3 tok/s | 9444 ms | 4K |
How Qwen 2.5 Math 72B (72B params) fits at each quantization level on RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | B56 |
Q3_K_S | 3 | 35.3 GB | Low | B57 |
NVFP4 | 4 |
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
Yes, RTX PRO 6000 Blackwell Workstation Edition 96GB can run Qwen 2.5 Math 72B with a B grade (Runs well). Expected decode speed: 34.3 tok/s.
Qwen 2.5 Math 72B (72B parameters) requires approximately 59.3 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 RTX PRO 6000 Blackwell Workstation Edition 96GB, Qwen 2.5 Math 72B achieves approximately 34.3 tokens per second decode speed with a time-to-first-token of 5649ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 Math 72B on RTX PRO 6000 Blackwell Workstation Edition 96GB receives a B grade with 34.3 tok/s and 4K context.
On RTX PRO 6000 Blackwell Workstation Edition 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-2.5-math-72b-on-rtx-pro-6000-blackwell-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
40.3 GB |
| Medium |
| B58 |
Q4_K_M | 4 | 43.9 GB | Medium | B59 |
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 |