Can Baichuan 7B run on NVIDIA T4 16GB?

YES — Tight Fit

B68Good

Estimated from fit model

Baichuan 7B needs ~14.9 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: 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

Q4_K_M (Medium quality) — 14.9 GB, 48.7 tok/s, Tight fit

14.9 GB required16.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

48.7 tok/s

TTFT

3974 ms

Safe context

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 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: 48.7 tok/s decode · 4.0s TTFT (warm) · 122 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.

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

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	48.7 tok/s	2168 ms	8K
Coding	B	Tight fit	48.7 tok/s	3974 ms	8K
Agentic Coding	F	Too heavy	16.5 tok/s	17077 ms	8K
Reasoning	B	Tight fit	48.7 tok/s	4697 ms	8K
RAG	F	Too heavy	16.5 tok/s	21346 ms	8K

Quantization options

How Baichuan 7B (7B params) fits at each quantization level on NVIDIA T4 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 99

Upgrade options

Hardware that runs Baichuan 7B well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)360 GB/s (+40)

Raises estimated decode speed by about 35%.65.8 tok/s decode

Raises estimated decode speed by about 35%.

Adds memory headroom for longer context windows and future model growth.

~$1,250 MSRP

RTX 3090 24GBBest value

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

Raises estimated decode speed by about 101%.98 tok/s decode

Raises estimated decode speed by about 101%.

Adds memory headroom for longer context windows and future model growth.

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

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

Raises estimated decode speed by about 101%.98 tok/s decode

Raises estimated decode speed by about 101%.

Adds memory headroom for longer context windows and future model growth.

~$1,599 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run Baichuan 7B?

Yes, NVIDIA T4 16GB can run Baichuan 7B with a B grade (Tight fit). Expected decode speed: 48.7 tok/s.

How much VRAM does Baichuan 7B need?

Baichuan 7B (7B parameters) requires approximately 14.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Baichuan 7B?

The recommended quantization for Baichuan 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Baichuan 7B run at on NVIDIA T4 16GB?

On NVIDIA T4 16GB, Baichuan 7B achieves approximately 48.7 tokens per second decode speed with a time-to-first-token of 3974ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run Baichuan 7B for coding?

For coding workloads, Baichuan 7B on NVIDIA T4 16GB receives a B grade with 48.7 tok/s and 8K context.

What context window can Baichuan 7B use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, Baichuan 7B can safely use up to 8K 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 7B feels slow on NVIDIA T4 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA T4 16GB See all hardware for Baichuan 7B

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