Can Yi 1.5 9B run on RTX 4500 Ada 24GB?

YES — Runs Great

C55Usable

Estimated from fit model

Yi 1.5 9B needs ~10.6 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~68 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, 67.6 tok/s, Runs well

10.6 GB required24.0 GB available

44% VRAM used

Fit status

Runs well

Decode

67.6 tok/s

TTFT

2864 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 RTX 4500 Ada 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: 67.6 tok/s decode · 2.9s TTFT (warm) · 169 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	67.6 tok/s	1562 ms	4K
Coding	C	Runs well	67.6 tok/s	2864 ms	4K
Agentic Coding	B	Runs well	67.6 tok/s	4165 ms	4K
Reasoning	C	Runs well	67.6 tok/s	3384 ms	4K
RAG	B	Runs well	67.6 tok/s	5207 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on RTX 4500 Ada 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	5.0 GB	Medium	C50
Q4_K_M	4	5.5 GB	Medium	C50
Q5_K_M	5	6.5 GB	High	C51
Q6_K	6	7.4 GB	High	C51
Q8_0	8	9.6 GB	Very High	C53
F16Best for your GPU	16	18.5 GB	Maximum	C54

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

Frequently asked questions

Can RTX 4500 Ada 24GB run Yi 1.5 9B?

Yes, RTX 4500 Ada 24GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 67.6 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 RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Yi 1.5 9B achieves approximately 67.6 tokens per second decode speed with a time-to-first-token of 2864ms using Q4_K_M quantization.

Can RTX 4500 Ada 24GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on RTX 4500 Ada 24GB receives a C grade with 67.6 tok/s and 4K context.

What context window can Yi 1.5 9B use on RTX 4500 Ada 24GB?

On RTX 4500 Ada 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 RTX 4500 Ada 24GB See all hardware for Yi 1.5 9B

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<iframe src="https://willitrunai.com/embed/yi-1.5-9b-on-rtx-4500-ada-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

67.6 tok/s

1562 ms

Coding

Runs well

67.6 tok/s

2864 ms

Agentic Coding

Runs well

67.6 tok/s

4165 ms

Reasoning

Runs well

67.6 tok/s

3384 ms

RAG

Runs well

67.6 tok/s

5207 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

C49

Q3_K_S

4.4 GB

Low

C50

NVFP4

5.0 GB

Medium

C50

Q4_K_M

5.5 GB

Medium

C50

Q5_K_M

6.5 GB

High

C51

Q6_K

7.4 GB

High

C51

Q8_0

9.6 GB

Very High

C53

F16Best for your GPU

18.5 GB

Maximum

C54

Frequently asked questions

Can RTX 4500 Ada 24GB run Yi 1.5 9B?

Yes, RTX 4500 Ada 24GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 67.6 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 RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Yi 1.5 9B achieves approximately 67.6 tokens per second decode speed with a time-to-first-token of 2864ms using Q4_K_M quantization.

Can RTX 4500 Ada 24GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on RTX 4500 Ada 24GB receives a C grade with 67.6 tok/s and 4K context.

What context window can Yi 1.5 9B use on RTX 4500 Ada 24GB?

On RTX 4500 Ada 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.