Qwen 2.5 Math 72B needs ~54.5 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~8 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
6.5 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~5.2 GB host RAM)
Decode
8.3 tok/s
TTFT
23337 ms
Safe context
4K
Memory
54.5 GB / 48.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 5.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Very compromised (needs ~3.4 GB host RAM) | 9.1 tok/s | 11559 ms | 4K |
| Coding | C | Very compromised (needs ~5.2 GB host RAM) | 8.3 tok/s | 23337 ms | 4K |
| Agentic Coding | F | Too heavy | 6.9 tok/s | 40665 ms | 4K |
| Reasoning | C | Very compromised (needs ~5.2 GB host RAM) | 8.3 tok/s | 27580 ms | 4K |
| RAG | F | Too heavy | 6.9 tok/s | 50831 ms |
How Qwen 2.5 Math 72B (72B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | B61 |
Q3_K_SBest for your GPU | 3 | 35.3 GB | Low | B61 |
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
NVIDIA A16 64GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 40%.
~$6,500 MSRP
RTX PRO 6000 Blackwell Workstation Edition 96GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 349%.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 300%.
~$9,999 MSRP
Yes, RTX A6000 48GB can run Qwen 2.5 Math 72B with a C grade (Very compromised (needs ~5.2 GB host RAM)). Expected decode speed: 8.3 tok/s.
Qwen 2.5 Math 72B (72B parameters) requires approximately 54.5 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 A6000 48GB, Qwen 2.5 Math 72B achieves approximately 8.3 tokens per second decode speed with a time-to-first-token of 23337ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 Math 72B on RTX A6000 48GB receives a C grade with 8.3 tok/s and 4K context.
On RTX A6000 48GB, 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-a6000-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
40.3 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 43.9 GB | Medium | F0 |
Q5_K_M | 5 | 51.8 GB | High | F0 |
Q6_K | 6 | 59.0 GB | High | F0 |
Q8_0 | 8 | 77.0 GB | Very High | F0 |
F16 | 16 | 147.6 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.