20 GB VRAM (+4)360 GB/s (+160)
A
Raises estimated decode speed by about 80%.65.8 tok/s decode
Raises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can Baichuan 7B run on NVIDIA A2 16GB?
YES — Tight Fit
B67Good
○Estimated from fit model
Baichuan 7B needs ~14.9 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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
Q4_K_M (Medium quality) — 14.9 GB, 36.5 tok/s, Tight fit
14.9 GB required16.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
36.5 tok/s
TTFT
5299 ms
Safe context
8K
Memory
14.9 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsBaichuan 7B on NVIDIA A2 16GB
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: 36.5 tok/s decode · 5.3s TTFT (warm) · 91 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 | B | Runs well | 36.5 tok/s | 2890 ms | 8K |
| Coding | B | Tight fit | 36.5 tok/s | 5299 ms | 8K |
| Agentic Coding | F | Too heavy | 13.1 tok/s | 21450 ms | 8K |
| Reasoning | B | Tight fit | 36.5 tok/s | 6263 ms | 8K |
| RAG | F | Too heavy | 13.1 tok/s | 26813 ms | 8K |
Quantization options
How Baichuan 7B (7B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B63 |
Q3_K_S | 3 | 3.4 GB | Low | B63 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Baichuan 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "baichuan-inc/Baichuan-7B" \
--hf-file "Baichuan-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Hardware that runs Baichuan 7B well
24 GB VRAM (+8)936 GB/s (+736)
A
Raises estimated decode speed by about 168%.98 tok/s decode
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
24 GB VRAM (+8)672 GB/s (+472)
A
Raises estimated decode speed by about 168%.98 tok/s decode
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP