96 GB VRAM (+16)4000 GB/s (+2065)
B
Raises estimated decode speed by about 114%.80.2 tok/s decode
Raises estimated decode speed by about 114%.
~$12,000 MSRP
Can Qwen 2.5 Math 72B run on NVIDIA A800 80GB?
YES — Runs Great
B67Good
○Estimated from fit model
Qwen 2.5 Math 72B needs ~57.7 GB VRAM. NVIDIA A800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 57.7 GB, 37.4 tok/s, Runs well
57.7 GB required80.0 GB available
72% VRAM used
Fit status
Runs well
Decode
37.4 tok/s
TTFT
5180 ms
Safe context
4K
Memory
57.7 GB / 80.0 GB
Memory breakdown
Weights43.9 GB
KV Cache4.9 GB
Runtime0.9 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsQwen 2.5 Math 72B on NVIDIA A800 80GB
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: 37.4 tok/s decode · 5.2s TTFT (warm) · 93 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 | Runs well | 37.4 tok/s | 2826 ms | 4K |
| Coding | B | Runs well | 37.4 tok/s | 5180 ms | 4K |
| Agentic Coding | B | Runs well | 37.4 tok/s | 7535 ms | 4K |
| Reasoning | B | Runs well | 37.4 tok/s | 6122 ms | 4K |
| RAG | B | Runs well | 37.4 tok/s | 9419 ms | 4K |
Quantization options
How Qwen 2.5 Math 72B (72B params) fits at each quantization level on NVIDIA A800 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | B57 |
Q3_K_S | 3 | 35.3 GB | Low | B59 |
NVFP4 | 4 | 40.3 GB | Medium | B61 |
Q4_K_M | 4 | 43.9 GB | Medium | B61 |
Q5_K_M | 5 | 51.8 GB | High | B61 |
Q6_KBest for your GPU | 6 | 59.0 GB | High | B61 |
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