Baichuan 13B needs ~24.9 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q5_K_M quantization, expect ~44 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
0.9 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
43.8 tok/s
TTFT
4425 ms
Safe context
8K
Memory
24.9 GB / 24.0 GB
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.
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 | A | Runs well | 61.5 tok/s | 1717 ms | 8K |
| Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 43.8 tok/s | 4425 ms | 8K |
| Agentic Coding | F | Too heavy | 19.4 tok/s | 14536 ms | 8K |
| Reasoning | B | Runs with offload (needs ~0.3 GB host RAM) | 43.8 tok/s | 5229 ms | 8K |
| RAG | F | Too heavy | 19.4 tok/s | 18170 ms |
How Baichuan 13B (13B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B62 |
Q3_K_S | 3 | 6.4 GB | Low | B62 |
NVFP4 | 4 |
Copy-paste commands to run Baichuan 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "baichuan-inc/Baichuan-13B-Chat" \
--hf-file "Baichuan-13B-Chat-Q5_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 190%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRaises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Yes, RTX PRO 4000 Blackwell 24GB can run Baichuan 13B with a B grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 43.8 tok/s.
Baichuan 13B (13B parameters) requires approximately 24.9 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 PRO 4000 Blackwell 24GB, Baichuan 13B achieves approximately 43.8 tokens per second decode speed with a time-to-first-token of 4425ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on RTX PRO 4000 Blackwell 24GB receives a B grade with 43.8 tok/s and 8K context.
On RTX PRO 4000 Blackwell 24GB, Baichuan 13B can safely use up to 8K 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-pro-4000-blackwell-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
7.3 GB |
| Medium |
| B63 |
Q4_K_M | 4 | 7.9 GB | Medium | B63 |
Q5_K_M | 5 | 9.4 GB | High | B64 |
Q6_K | 6 | 10.7 GB | High | B65 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | B66 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.