Can Yi 1.5 9B run on NVIDIA L4 24GB?

YES — Runs Great

C53Usable

Estimated from fit model

Yi 1.5 9B needs ~10.6 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~39 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) — 10.6 GB, 38.6 tok/s, Runs well

10.6 GB required24.0 GB available

44% VRAM used

Fit status

Runs well

Decode

38.6 tok/s

TTFT

5012 ms

Safe context

Memory

10.6 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on NVIDIA L4 24GB
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: 38.6 tok/s decode · 5.0s TTFT (warm) · 97 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	C	Runs well	38.6 tok/s	2734 ms	4K
Coding	C	Runs well	38.6 tok/s	5012 ms	4K
Agentic Coding	C	Runs well	38.6 tok/s	7290 ms	4K
Reasoning	C	Runs well	38.6 tok/s	5923 ms	4K
RAG	C	Runs well	38.6 tok/s	9113 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C49
Q3_K_S	3	4.4 GB	Low	C50
NVFP4	4

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

Upgrade options

Hardware that runs Yi 1.5 9B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 226%.126 tok/s decode

Raises estimated decode speed by about 226%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 226%.126 tok/s decode

Raises estimated decode speed by about 226%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)576 GB/s (+276)

Raises estimated decode speed by about 137%.91.3 tok/s decode

Raises estimated decode speed by about 137%.

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

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Yi 1.5 9B?

Yes, NVIDIA L4 24GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 38.6 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 10.6 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 L4 24GB?

On NVIDIA L4 24GB, Yi 1.5 9B achieves approximately 38.6 tokens per second decode speed with a time-to-first-token of 5012ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on NVIDIA L4 24GB receives a C grade with 38.6 tok/s and 4K context.

What context window can Yi 1.5 9B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, 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 L4 24GB See all hardware for Yi 1.5 9B

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