Baichuan 13B needs ~24.4 GB but RTX 6000 Ada Laptop 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
8.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
14.2 tok/s
TTFT
13656 ms
Safe context
5K
Memory
24.4 GB / 16.0 GB
Offload
30%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 24.4 GB, but this setup only exposes 16.0 GB of usable VRAM.
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 | B | Very compromised (needs ~1.2 GB host RAM) | 26.0 tok/s | 4060 ms | 5K |
| Coding | F | Too heavy | 14.2 tok/s | 13656 ms | 5K |
| Agentic Coding | F | Too heavy | 6.9 tok/s | 40971 ms | 5K |
| Reasoning | F | Too heavy | 14.2 tok/s | 16139 ms | 5K |
| RAG | F | Too heavy | 6.9 tok/s | 51213 ms | 5K |
How Baichuan 13B (13B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B65 |
Q3_K_S | 3 | 6.4 GB | Low | B66 |
NVFP4 | 4 |
Upgrade options
RTX 3090 24GBBudget 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.
~$1,499 MSRP
RTX 4090 24GBBest 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.
~$1,599 MSRP
RTX 5090 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.
~$1,999 MSRP
No, Baichuan 13B requires more memory than RTX 6000 Ada Laptop 16GB provides.
Baichuan 13B (13B parameters) requires approximately 24.4 GB of memory with Q5_K_M quantization.
The recommended quantization for Baichuan 13B is Q5_K_M, which balances quality and memory efficiency.
On RTX 6000 Ada Laptop 16GB, Baichuan 13B achieves approximately 14.2 tokens per second decode speed with a time-to-first-token of 13656ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on RTX 6000 Ada Laptop 16GB receives a F grade with 14.2 tok/s and 5K context.
On RTX 6000 Ada Laptop 16GB, Baichuan 13B can safely use up to 5K tokens of context. The model's official context limit is 8K, 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/baichuan-13b-on-rtx-6000-ada-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
7.3 GB |
| Medium |
| B67 |
Q4_K_M | 4 | 7.9 GB | Medium | B68 |
Q5_K_M | 5 | 9.4 GB | High | B67 |
Q6_KBest for your GPU | 6 | 10.7 GB | High | B67 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 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.
