32 GB VRAM (+8)1792 GB/s (+1192)
A
Raises estimated decode speed by about 257%.126.9 tok/s decode
Raises estimated decode speed by about 257%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Baichuan 13B run on NVIDIA A10 24GB?
YES — With Offload
B67Good
○Estimated from fit model
Baichuan 13B needs ~24.9 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q5_K_M quantization, expect ~36 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
Share:
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, 35.5 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
35.5 tok/s
TTFT
5454 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 NVIDIA A10 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: 35.5 tok/s decode · 5.5s TTFT (warm) · 89 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 | 51.0 tok/s | 2071 ms | 8K |
| Coding | B | Runs with offload | 35.5 tok/s | 5454 ms | 8K |
| Agentic Coding | F | Too heavy | 15.3 tok/s | 18389 ms | 8K |
| Reasoning | B | Runs with offload | 35.5 tok/s | 6446 ms | 8K |
| RAG | F | Too heavy | 15.3 tok/s | 22986 ms | 8K |
Quantization options
How Baichuan 13B (13B params) fits at each quantization level on NVIDIA A10 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 |
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 99Upgrade options
Hardware that runs Baichuan 13B well
32 GB VRAM (+8)896 GB/s (+296)
A
Raises estimated decode speed by about 131%.82 tok/s decode
Raises estimated decode speed by about 131%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)
A
Raises estimated decode speed by about 41%.50.2 tok/s decode
Raises estimated decode speed by about 41%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP