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

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.

Capabilities:

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

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 99

Upgrade options

Hardware that runs Baichuan 13B well

NVIDIA A100 80GBBudget pick

80 GB VRAM (+16)2039 GB/s (+1439)

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

NVIDIA A800 80GBBest value

80 GB VRAM (+16)1935 GB/s (+1335)

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

NVIDIA H800 80GBNVIDIA upgrade

80 GB VRAM (+16)3000 GB/s (+2400)

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

Frequently asked questions

Can NVIDIA A16 64GB run Baichuan 13B?

Yes, NVIDIA A16 64GB can run Baichuan 13B with a B grade (Runs well). Expected decode speed: 51.0 tok/s.

How much VRAM does Baichuan 13B need?

Baichuan 13B (13B parameters) requires approximately 29.2 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 NVIDIA A16 64GB?

On NVIDIA A16 64GB, Baichuan 13B achieves approximately 51.0 tokens per second decode speed with a time-to-first-token of 3796ms using Q5_K_M quantization.

Can NVIDIA A16 64GB run Baichuan 13B for coding?

For coding workloads, Baichuan 13B on NVIDIA A16 64GB receives a B grade with 51.0 tok/s and 8K context.

What context window can Baichuan 13B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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.

See all results for NVIDIA A16 64GB See all hardware for Baichuan 13B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/baichuan-13b-on-a16-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: