Can Qwen 2.5 72B run on NVIDIA H200 PCIe 141GB?
YES — Runs Great
Qwen 2.5 72B needs ~64.1 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~100 tok/s.
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
Fit status
Runs well
Decode
99.8 tok/s
TTFT
1939 ms
Safe context
131K
Memory
64.1 GB / 141.0 GB
Memory breakdown
See how fast it feels
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 | A | Runs well | 99.8 tok/s | 1058 ms | 131K |
| Coding | A | Runs well | 99.8 tok/s | 1939 ms | 131K |
| Agentic Coding | A | Runs well | 99.8 tok/s | 2821 ms | 131K |
| Reasoning | A | Runs well | 99.8 tok/s | 2292 ms | 131K |
| RAG | A | Runs well | 99.8 tok/s | 3526 ms | 131K |
Inference speed
Qwen 2.5 72B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Qwen 2.5 72B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~17 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 16.7 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 14.9 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 13.8 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 11.5 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 11.0 | Too big |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 10.9 | Fits |
| 48 GB | Q4_K_M | 8.8 | Heavy offload | |
| 32 GB | Q4_K_M | 8.1 | Too big | |
| 48 GB | Q4_K_M | 8.1 | Heavy offload | |
| 48 GB | Q4_K_M | 7.1 | Heavy offload | |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 5.1 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 4.4 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 4.0 | Too big |
| 24 GB | Q4_K_M | 2.8 | Too big | |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.6 | Too big |
| 24 GB | Q4_K_M | 2.4 | Too big | |
| 16 GB | Q4_K_M | 2.3 | Too big | |
| 12 GB | Q4_K_M | 2.0 | Too big | |
| 12 GB | Q4_K_M | 2.0 | Too big | |
| 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Qwen 2.5 72B (72B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | A71 |
Q3_K_S | 3 | 35.3 GB | Low | A72 |
NVFP4 | 4 | 40.3 GB | Medium | A73 |
Q4_K_M | 4 | 43.9 GB | Medium | A73 |
Q5_K_M | 5 | 51.8 GB | High | A75 |
Q6_K | 6 | 59.0 GB | High | A76 |
Q8_0Best for your GPU | 8 | 77.0 GB | Very High | A78 |
F16 | 16 | 147.6 GB | Maximum | F0 |
Get started
Copy-paste commands to run Qwen 2.5 72B on your machine.
Run
ollama run qwen2.5:72bYour hardware
More models your NVIDIA H200 PCIe 141GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 123B | S | 58.4 tok/s | ||
| 122B | S | 162.1 tok/s | ||
| 119B | S | 175.8 tok/s | ||
| 117B | S | 61.4 tok/s | ||
| 111B | S | 65 tok/s |
Frequently asked questions
Can NVIDIA H200 PCIe 141GB run Qwen 2.5 72B?
Yes, NVIDIA H200 PCIe 141GB can run Qwen 2.5 72B with a A grade (Runs well). Expected decode speed: 99.8 tok/s.
How much VRAM does Qwen 2.5 72B need?
Qwen 2.5 72B (72B parameters) requires approximately 64.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen 2.5 72B?
The recommended quantization for Qwen 2.5 72B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen 2.5 72B run at on NVIDIA H200 PCIe 141GB?
On NVIDIA H200 PCIe 141GB, Qwen 2.5 72B achieves approximately 99.8 tokens per second decode speed with a time-to-first-token of 1939ms using Q4_K_M quantization.
Can NVIDIA H200 PCIe 141GB run Qwen 2.5 72B for coding?
For coding workloads, Qwen 2.5 72B on NVIDIA H200 PCIe 141GB receives a A grade with 99.8 tok/s and 131K context.
What context window can Qwen 2.5 72B use on NVIDIA H200 PCIe 141GB?
On NVIDIA H200 PCIe 141GB, Qwen 2.5 72B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/qwen-2.5-72b-on-h200-pcie-141gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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