Can Yi 1.5 9B run on NVIDIA T4 16GB?

YES — Runs Great

B57Good

Estimated from fit model

Yi 1.5 9B needs ~9.8 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 9.8 GB, 41.2 tok/s, Runs well

9.8 GB required16.0 GB available

61% VRAM used

Fit status

Runs well

Decode

41.2 tok/s

TTFT

4699 ms

Safe context

Memory

9.8 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on NVIDIA T4 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 41.2 tok/s decode · 4.7s TTFT (warm) · 103 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	37.9 tok/s	2787 ms	4K
Coding	B	Runs well	37.9 tok/s	5110 ms	4K
Agentic Coding	B	Runs well	37.9 tok/s	7433 ms	4K
Reasoning	B	Runs well	37.9 tok/s	6039 ms	4K
RAG	B	Runs well	37.9 tok/s	9291 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C52
Q3_K_S	3	4.4 GB	Low	C53
NVFP4	4	5.0 GB	Medium	C53
Q4_K_M	4	5.5 GB	Medium	C54
Q5_K_M	5	6.5 GB	High	C55
Q6_K	6	7.4 GB	High	B56
Q8_0Best for your GPU	8	9.6 GB	Very High	B56
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run Yi 1.5 9B on your machine.

Run

lms load Yi-1.5-9B-Chat && lms server start

アップグレードオプション

Yi 1.5 9Bを快適に動かすハードウェア

RX 7900 XT 20GBコスパ最良

20 GB VRAM (+4)800 GB/s (+480)

Raises estimated decode speed by about 131%.95.1 tok/sデコード

Raises estimated decode speed by about 131%.

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

〜$899 MSRP

RTX A4500 20GBコスパ重視

20 GB VRAM (+4)640 GB/s (+320)

Raises estimated decode speed by about 140%.98.9 tok/sデコード

Raises estimated decode speed by about 140%.

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

〜$2,000 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run Yi 1.5 9B?

Yes, NVIDIA T4 16GB can run Yi 1.5 9B with a B grade (Runs well). Expected decode speed: 37.9 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 9.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 9B?

The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 9B run at on NVIDIA T4 16GB?

On NVIDIA T4 16GB, Yi 1.5 9B achieves approximately 37.9 tokens per second decode speed with a time-to-first-token of 5110ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on NVIDIA T4 16GB receives a B grade with 37.9 tok/s and 4K context.

What context window can Yi 1.5 9B use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, Yi 1.5 9B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for NVIDIA T4 16GB See all hardware for Yi 1.5 9B

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