96 GB VRAM (+32)1792 GB/s (+1192)
B
Raises estimated decode speed by about 222%.37.3 tok/s decode
Raises estimated decode speed by about 222%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
Can Qwen 2.5 Math 72B run on NVIDIA A16 64GB?
YES — Tight Fit
B61Good
○Estimated from fit model
Qwen 2.5 Math 72B needs ~56.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~12 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 56.1 GB, 11.6 tok/s, Tight fit
56.1 GB required64.0 GB available
88% VRAM used
Fit status
Tight fit
Decode
11.6 tok/s
TTFT
16707 ms
Safe context
4K
Memory
56.1 GB / 64.0 GB
Memory breakdown
Weights43.9 GB
KV Cache4.9 GB
Runtime0.9 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsQwen 2.5 Math 72B on NVIDIA A16 64GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 11.6 tok/s decode · 16.7s TTFT (warm) · 29 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 11.6 tok/s | 9113 ms | 4K |
| Coding | B | Tight fit | 11.6 tok/s | 16707 ms | 4K |
| Agentic Coding | B | Runs with offload | 11.6 tok/s | 24301 ms | 4K |
| Reasoning | B | Tight fit | 11.6 tok/s | 19744 ms | 4K |
| RAG | B | Runs with offload | 11.6 tok/s | 30376 ms | 4K |
Quantization options
How Qwen 2.5 Math 72B (72B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | B60 |
Q3_K_S | 3 | 35.3 GB | Low | B61 |
NVFP4 | 4 | 40.3 GB | Medium | B61 |
Q4_K_M | 4 | 43.9 GB | Medium | B61 |
Q5_K_MBest for your GPU | 5 | 51.8 GB | High | B61 |
Q6_K | 6 | 59.0 GB | High | F0 |
Q8_0 | 8 | 77.0 GB | Very High | F0 |
F16 | 16 | 147.6 GB | Maximum | F0 |
Get started
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
Hardware that runs Qwen 2.5 Math 72B well
96 GB VRAM (+32)1597 GB/s (+997)
B
Raises estimated decode speed by about 186%.33.2 tok/s decode
Raises estimated decode speed by about 186%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
96 GB VRAM (+32)4000 GB/s (+3400)
B
Raises estimated decode speed by about 591%.80.2 tok/s decode
Raises estimated decode speed by about 591%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP