Can Baichuan 13B run on NVIDIA T4 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Baichuan 13B needs ~24.4 GB but NVIDIA T4 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 24.4 GB, exceeds 16.0 GB available

24.4 GB required16.0 GB available

153% VRAM needed

8.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.6 tok/s

TTFT

29493 ms

Safe context

Memory

24.4 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights9.4 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsBaichuan 13B on NVIDIA T4 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: 6.6 tok/s decode · 29.5s TTFT (warm) · 16 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 24.4 GB, but this setup only exposes 16.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Very compromised	12.4 tok/s	8541 ms	5K
Coding	F	Too heavy	6.6 tok/s	29493 ms	5K
Agentic Coding	F	Too heavy	3.4 tok/s	82824 ms	5K
Reasoning	F	Too heavy	6.6 tok/s	34855 ms	5K
RAG	F	Too heavy	3.4 tok/s	103529 ms	5K

Quantization options

How Baichuan 13B (13B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B65
Q3_K_S	3	6.4 GB	Low	B66
NVFP4	4

Upgrade options

Hardware that runs Baichuan 13B well

RTX 3090 24GBBudget pick

24 GB VRAM (+8)936 GB/s (+616)

Makes the model fit on the accelerator instead of staying completely out of reach.48.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+8)1008 GB/s (+688)

Makes the model fit on the accelerator instead of staying completely out of reach.56.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.42.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run Baichuan 13B?

No, Baichuan 13B requires more memory than NVIDIA T4 16GB provides.

How much VRAM does Baichuan 13B need?

Baichuan 13B (13B parameters) requires approximately 24.4 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 T4 16GB?

On NVIDIA T4 16GB, Baichuan 13B achieves approximately 6.6 tokens per second decode speed with a time-to-first-token of 29493ms using Q5_K_M quantization.

Can NVIDIA T4 16GB run Baichuan 13B for coding?

For coding workloads, Baichuan 13B on NVIDIA T4 16GB receives a F grade with 6.6 tok/s and 5K context.

What context window can Baichuan 13B use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, Baichuan 13B can safely use up to 5K 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 NVIDIA T4 16GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for NVIDIA T4 16GB See all hardware for Baichuan 13B

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