Qwen 2.5 14B needs ~15.1 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~68 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
68.0 tok/s
TTFT
2848 ms
Safe context
65K
Memory
15.1 GB / 24.0 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 | A | Runs well | 68.0 tok/s | 1553 ms | 65K |
| Coding | S | Runs well | 68.0 tok/s | 2848 ms | 65K |
| Agentic Coding | S | Runs well | 68.0 tok/s | 4142 ms | 65K |
| Reasoning | S | Runs well | 68.0 tok/s | 3365 ms | 65K |
| RAG | S | Runs well | 68.0 tok/s | 5177 ms | 65K |
How Qwen 2.5 14B (14B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A77 |
Q3_K_S | 3 | 6.9 GB | Low | A77 |
NVFP4 | 4 | 7.8 GB | Medium | A78 |
Q4_K_M | 4 | 8.5 GB | Medium | A78 |
Q5_K_M | 5 | 10.1 GB | High | A79 |
Q6_K | 6 | 11.5 GB | High | A80 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | A81 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Copy-paste commands to run Qwen 2.5 14B on your machine.
Run
ollama run qwen2.5Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 81.3 tok/s | |
| Qwen 3.5 27B | 27B | S | 35.3 tok/s | |
| Qwen 3.6 27B | 27B | S | 35.4 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 84.1 tok/s | |
| Qwen 3.5 35B A3B | 35B | A | 45.5 tok/s |
Yes, RTX A5000 24GB can run Qwen 2.5 14B with a S grade (Runs well). Expected decode speed: 68.0 tok/s.
Qwen 2.5 14B (14B parameters) requires approximately 15.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 14B is Q4_K_M, which balances quality and memory efficiency.
On RTX A5000 24GB, Qwen 2.5 14B achieves approximately 68.0 tokens per second decode speed with a time-to-first-token of 2848ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 14B on RTX A5000 24GB receives a S grade with 68.0 tok/s and 65K context.
On RTX A5000 24GB, Qwen 2.5 14B can safely use up to 65K tokens of context. The model's official context limit is 131K, 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-2.5-14b-on-a5000-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: