Can Yi 1.5 9B run on RTX A6000 48GB?

YES — Runs Great

C52Usable

○Estimated from fit model

Yi 1.5 9B needs ~13.0 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~116 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) — 13.0 GB, 115.6 tok/s, Runs well

13.0 GB required48.0 GB available

27% VRAM used

Fit status

Runs well

Decode

115.6 tok/s

TTFT

1674 ms

Safe context

Memory

13.0 GB / 48.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on RTX A6000 48GB
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: 115.6 tok/s decode · 1.7s TTFT (warm) · 289 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	115.6 tok/s	913 ms	4K
Coding	C	Runs well	115.6 tok/s	1674 ms	4K
Agentic Coding	C	Runs well	115.6 tok/s	2436 ms	4K
Reasoning	C	Runs well	115.6 tok/s	1979 ms	4K
RAG	C	Runs well	115.6 tok/s	3044 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C46
Q3_K_S	3	4.4 GB	Low	C46
NVFP4	4	5.0 GB	Medium	C46
Q4_K_M	4	5.5 GB	Medium	C46
Q5_K_M	5	6.5 GB	High	C46
Q6_K	6	7.4 GB	High	C47
Q8_0	8	9.6 GB	Very High	C47
F16Best for your GPU	16	18.5 GB	Maximum	C50

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 A6000 48GB See all hardware for Yi 1.5 9B

URL: https://willitrunai.com/can-run/yi-1.5-9b-on-a6000-48gb