Qwen3.5 35B A3B needs ~28.3 GB but Tesla P100 16GB only has 16.0 GB. Try a smaller quantization or lighter model.
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
12.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.2 tok/s
TTFT
45731 ms
Safe context
4K
Memory
28.3 GB / 16.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 28.3 GB, but this setup only exposes 16.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 5.0 tok/s | 21140 ms | 4K |
| Coding | F | Too heavy | 4.2 tok/s | 45731 ms | 4K |
| Agentic Coding | F | Too heavy | 3.1 tok/s | 89583 ms | 4K |
| Reasoning | F | Too heavy | 4.2 tok/s | 54046 ms | 4K |
| RAG | F | Too heavy | 3.1 tok/s | 111978 ms | 4K |
How Qwen3.5 35B A3B (35B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | F0 |
Q3_K_S | 3 | 17.2 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX 5090 32GBBudget pickMakes 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.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueMakes 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.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeMakes 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.
~$4,000 MSRP
No, Qwen3.5 35B A3B requires more memory than Tesla P100 16GB provides.
Qwen3.5 35B A3B (35B parameters) requires approximately 28.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.
On Tesla P100 16GB, Qwen3.5 35B A3B achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 45731ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 35B A3B on Tesla P100 16GB receives a F grade with 4.2 tok/s and 4K context.
On Tesla P100 16GB, Qwen3.5 35B A3B can safely use up to 4K tokens of context. The model's official context limit is —, 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/hf-unsloth--qwen3-5-35b-a3b-gguf-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>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 21.3 GB | Medium | F0 |
Q5_K_M | 5 | 25.2 GB | High | F0 |
Q6_K | 6 | 28.7 GB | High | F0 |
Q8_0 | 8 | 37.5 GB | Very High | F0 |
F16 | 16 | 71.8 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
