RTX 5090 32GB经济之选32 GB VRAM (+8)1792 GB/s (+1024)
A
Raises estimated decode speed by about 211%.126.9 tok/s 解码
Raises estimated decode speed by about 211%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Baichuan 13B run on RTX A5000 24GB?
YES — With Offload
B67Good
Estimated from fit model
Baichuan 13B needs ~24.9 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q5_K_M quantization, expect ~41 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q5_K_M (High quality) — 24.9 GB, 40.8 tok/s, Runs with offload (needs ~0.3 GB host RAM)
24.9 GB required24.0 GB available
104% VRAM needed
0.9 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
40.8 tok/s
TTFT
4748 ms
Safe context
8K
Memory
24.9 GB / 24.0 GB
Memory breakdown
Weights9.4 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsBaichuan 13B on RTX A5000 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 40.8 tok/s decode · 4.7s TTFT (warm) · 102 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 58.6 tok/s | 1803 ms | 8K |
| Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 40.8 tok/s | 4748 ms | 8K |
| Agentic Coding | F | Too heavy | 17.6 tok/s | 16008 ms | 8K |
| Reasoning | B | Runs with offload (needs ~0.3 GB host RAM) | 40.8 tok/s | 5611 ms | 8K |
| RAG | F | Too heavy | 17.6 tok/s | 20010 ms | 8K |
Quantization options
How Baichuan 13B (13B params) fits at each quantization level on RTX A5000 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 | 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 |
Get started
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 99升级选项
能流畅运行 Baichuan 13B 的硬件
RTX 5090 32GB经济之选RTX PRO 4500 Blackwell 32GB最佳性价比32 GB VRAM (+8)896 GB/s (+128)
A
Raises estimated decode speed by about 101%.82 tok/s 解码
Raises estimated decode speed by about 101%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA 升级32 GB VRAM (+8)
A
Adds memory headroom for longer context windows and future model growth.50.2 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Frequently asked questions
Can RTX A5000 24GB run Baichuan 13B?
Yes, RTX A5000 24GB can run Baichuan 13B with a B grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 40.8 tok/s.
How much VRAM does Baichuan 13B need?
Baichuan 13B (13B parameters) requires approximately 24.9 GB of memory with Q5_K_M quantization.
What is the best quantization for Baichuan 13B?
The recommended quantization for Baichuan 13B is Q5_K_M, which balances quality and memory efficiency.
What speed will Baichuan 13B run at on RTX A5000 24GB?
On RTX A5000 24GB, Baichuan 13B achieves approximately 40.8 tokens per second decode speed with a time-to-first-token of 4748ms using Q5_K_M quantization.
Can RTX A5000 24GB run Baichuan 13B for coding?
For coding workloads, Baichuan 13B on RTX A5000 24GB receives a B grade with 40.8 tok/s and 8K context.
What context window can Baichuan 13B use on RTX A5000 24GB?
On RTX A5000 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.
What should I upgrade first if Baichuan 13B feels slow on RTX A5000 24GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/baichuan-13b-on-a5000-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: