80 GB VRAM (+16)2039 GB/s (+1439)
B
Raises estimated decode speed by about 257%.182 tok/s decode
Raises estimated decode speed by about 257%.
Adds memory headroom for longer context windows and future model growth.
~$15,000 MSRP
Can Baichuan 13B run on NVIDIA A16 64GB?
YES — Runs Great
B66Good
○Estimated from fit model
Baichuan 13B needs ~29.2 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q5_K_M quantization, expect ~51 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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) — 29.2 GB, 51.0 tok/s, Runs well
29.2 GB required64.0 GB available
46% VRAM used
Fit status
Runs well
Decode
51.0 tok/s
TTFT
3796 ms
Safe context
8K
Memory
29.2 GB / 64.0 GB
Memory breakdown
Weights9.4 GB
KV Cache12.2 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsBaichuan 13B on NVIDIA A16 64GB
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: 51.0 tok/s decode · 3.8s TTFT (warm) · 128 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 51.0 tok/s | 2071 ms | 8K |
| Coding | B | Runs well | 51.0 tok/s | 3796 ms | 8K |
| Agentic Coding | B | Runs well | 51.0 tok/s | 5522 ms | 8K |
| Reasoning | B | Runs well | 51.0 tok/s | 4486 ms | 8K |
| RAG | B | Runs well | 51.0 tok/s | 6902 ms | 8K |
Quantization options
How Baichuan 13B (13B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B57 |
Q3_K_S | 3 | 6.4 GB | Low | B57 |
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
80 GB VRAM (+16)1935 GB/s (+1335)
B
Raises estimated decode speed by about 223%.164.5 tok/s decode
Raises estimated decode speed by about 223%.
Adds memory headroom for longer context windows and future model growth.
~$15,000 MSRP
80 GB VRAM (+16)3000 GB/s (+2400)
B
Raises estimated decode speed by about 257%.182 tok/s decode
Raises estimated decode speed by about 257%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP