Qwen 3 30B A3B needs ~16.2 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q2_K quantization, expect ~62 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
6.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
21.7 tok/s
TTFT
8906 ms
Safe context
4K
Memory
22.9 GB / 16.0 GB
Offload
30%
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 23.3 tok/s | 4523 ms | 4K |
| Coding | F | Too heavy | 21.7 tok/s | 8906 ms | 4K |
| Agentic Coding | F | Too heavy | 19.0 tok/s | 14846 ms | 4K |
| Reasoning | F | Too heavy | 21.7 tok/s | 10525 ms | 4K |
| RAG | F | Too heavy | 19.0 tok/s | 18558 ms | 4K |
How Qwen 3 30B A3B (30.5B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.9 GB | Low | F0 |
Q3_K_S | 3 | 14.9 GB | Low | F0 |
NVFP4 | 4 | 17.1 GB | Medium | F0 |
Q4_K_M | 4 | 18.6 GB | Medium | F0 |
Q5_K_M | 5 | 22.0 GB | High | F0 |
Q6_K | 6 | 25.0 GB | High | F0 |
Q8_0 | 8 | 32.6 GB | Very High | F0 |
F16 | 16 | 62.5 GB | Maximum | F0 |
Copy-paste commands to run Qwen 3 30B A3B on your machine.
Run
ollama run qwen3:30b-a3bアップグレードオプション
RTX 4000 Ada 20GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
〜$1,250 MSRP
RTX 3090 24GBコスパ重視Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$1,499 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIAアップグレードMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$1,599 MSRP
Yes, Tesla P100 16GB can run Qwen 3 30B A3B at Q2_K quantization (Runs with offload (needs ~0.1 GB host RAM)). The recommended Q4_K_M requires 22.9 GB which exceeds available memory, but at Q2_K it needs only 16.2 GB. Expected decode speed: 62.0 tok/s.
Qwen 3 30B A3B (30.5B parameters) requires approximately 22.9 GB at Q4_K_M quantization. On Tesla P100 16GB, it fits at Q2_K using 16.2 GB.
The recommended quantization is Q4_K_M, but on Tesla P100 16GB the best fitting quantization is Q2_K, which uses 16.2 GB.
On Tesla P100 16GB, Qwen 3 30B A3B achieves approximately 62.0 tokens per second decode speed with a time-to-first-token of 3124ms using Q2_K quantization.
For coding workloads, Qwen 3 30B A3B on Tesla P100 16GB receives a F grade with 21.7 tok/s and 4K context.
On Tesla P100 16GB, Qwen 3 30B A3B can safely use up to 14K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-3-30b-a3b-on-tesla-p100-16gb" 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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