NVIDIA H20 96GBBudget pick96 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
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.
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
37.4 tok/s
TTFT
5180 ms
Safe context
4K
Memory
57.7 GB / 80.0 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 | 37.4 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 |
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 |
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 H20 96GBBudget pickRaises estimated decode speed by about 114%.
~$12,000 MSRP
NVIDIA GH200 96GBBest valueRaises estimated decode speed by about 114%.
~$30,000 MSRP
Yes, NVIDIA A800 80GB can run Qwen 2.5 Math 72B with a B grade (Runs well). Expected decode speed: 37.4 tok/s.
Qwen 2.5 Math 72B (72B parameters) requires approximately 57.7 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 NVIDIA A800 80GB, Qwen 2.5 Math 72B achieves approximately 37.4 tokens per second decode speed with a time-to-first-token of 5180ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 Math 72B on NVIDIA A800 80GB receives a B grade with 37.4 tok/s and 4K context.
On NVIDIA A800 80GB, 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-a800-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: