Can Yi 1.5 9B run on RTX PRO 4000 Blackwell 24GB?

YES — Runs Great

B56Good

○Estimated from fit model

Yi 1.5 9B needs ~10.6 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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, 111.8 tok/s, Runs well

10.6 GB required24.0 GB available

44% VRAM used

Fit status

Runs well

Decode

111.8 tok/s

TTFT

1731 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 PRO 4000 Blackwell 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: 111.8 tok/s decode · 1.7s TTFT (warm) · 280 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	B	Runs well	111.8 tok/s	944 ms	4K
Coding	B	Runs well	111.8 tok/s	1731 ms	4K
Agentic Coding	B	Runs well	111.8 tok/s	2518 ms	4K
Reasoning	B	Runs well	111.8 tok/s	2046 ms	4K
RAG	B	Runs well	111.8 tok/s	3148 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on RTX PRO 4000 Blackwell 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

See all results for RTX PRO 4000 Blackwell 24GB See all hardware for Yi 1.5 9B

URL: https://willitrunai.com/can-run/yi-1.5-9b-on-rtx-pro-4000-blackwell-24gb

⇱ Yi 1.5 9B on RTX PRO 4000 Blackwell 24GB? YES