RTX A6000 48GBBest value48 GB VRAM (+8)
C
Makes the model fit on the accelerator instead of staying completely out of reach.8.3 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
~$4,650 MSRP
Qwen 2.5 Math 72B needs ~45.1 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q3_K_S quantization, expect ~22 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
13.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
13.0 tok/s
TTFT
14838 ms
Safe context
4K
Memory
53.7 GB / 40.0 GB
Offload
30%
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 4.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 13.2 tok/s | 7980 ms | 4K |
| Coding | F | Too heavy | 12.0 tok/s | 16136 ms | 4K |
| Agentic Coding | F | Too heavy | 10.0 tok/s | 28190 ms | 4K |
| Reasoning | F | Too heavy | 12.0 tok/s | 19070 ms | 4K |
| RAG | F | Too heavy | 10.0 tok/s | 35237 ms | 4K |
How Qwen 2.5 Math 72B (72B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 28.1 GB | Low | B61 |
Q3_K_S | 3 | 35.3 GB | Low | 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
RTX A6000 48GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 27%.
~$4,999 MSRP
NVIDIA A16 64GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$6,500 MSRP
NVIDIA H100 80GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$40,000 MSRP
Yes, NVIDIA A100 40GB can run Qwen 2.5 Math 72B at Q3_K_S quantization (Very compromised (needs ~4 GB host RAM)). The recommended Q4_K_M requires 53.7 GB which exceeds available memory, but at Q3_K_S it needs only 45.1 GB. Expected decode speed: 21.9 tok/s.
Qwen 2.5 Math 72B (72B parameters) requires approximately 53.7 GB at Q4_K_M quantization. On NVIDIA A100 40GB, it fits at Q3_K_S using 45.1 GB.
The recommended quantization is Q4_K_M, but on NVIDIA A100 40GB the best fitting quantization is Q3_K_S, which uses 45.1 GB.
On NVIDIA A100 40GB, Qwen 2.5 Math 72B achieves approximately 21.9 tokens per second decode speed with a time-to-first-token of 8859ms using Q3_K_S quantization.
For coding workloads, Qwen 2.5 Math 72B on NVIDIA A100 40GB receives a F grade with 12.0 tok/s and 4K context.
On NVIDIA A100 40GB, Qwen 2.5 Math 72B can safely use up to 4K tokens of context at Q3_K_S quantization. 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-a100-40gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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.