Baichuan 13B needs ~23.8 GB but RTX 3080 10GB only has 10.0 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
13.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
9.4 tok/s
TTFT
20505 ms
Safe context
4K
Memory
23.8 GB / 10.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 23.8 GB, but this setup only exposes 10.0 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 | F | Too heavy | 14.3 tok/s | 7409 ms | 4K |
| Coding | F | Too heavy | 9.4 tok/s | 20505 ms | 4K |
| Agentic Coding | F | Too heavy | 9.4 tok/s | 29826 ms | 4K |
| Reasoning | F | Too heavy | 9.4 tok/s | 24233 ms | 4K |
| RAG | F | Too heavy | 9.4 tok/s | 37282 ms | 4K |
How Baichuan 13B (13B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B69 |
Q3_K_SBest for your GPU | 3 | 6.4 GB | Low | B68 |
NVFP4 | 4 | 7.3 GB | Medium | F0 |
Q4_K_M | 4 | 7.9 GB | Medium | F0 |
Q5_K_M | 5 | 9.4 GB | High | F0 |
Q6_K | 6 | 10.7 GB | High | F0 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
升级选项
RTX 3090 24GB经济之选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 4090 24GB最佳性价比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
RTX PRO 4000 Blackwell 24GBNVIDIA 升级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
No, Baichuan 13B requires more memory than RTX 3080 10GB provides.
Baichuan 13B (13B parameters) requires approximately 23.8 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 3080 10GB, Baichuan 13B achieves approximately 9.4 tokens per second decode speed with a time-to-first-token of 20505ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on RTX 3080 10GB receives a F grade with 9.4 tok/s and 4K context.
On RTX 3080 10GB, Baichuan 13B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/baichuan-13b-on-rtx-3080-10gb" 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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