Can Yi 1.5 9B Chat run on RTX 4000 Ada Laptop 12GB?

YES — Runs Great

B55Good

Estimated from fit model

Yi 1.5 9B Chat needs ~8.9 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~57 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) — 8.9 GB, 57.4 tok/s, Runs well

8.9 GB required12.0 GB available

74% VRAM used

Fit status

Runs well

Decode

57.4 tok/s

TTFT

3370 ms

Safe context

62K

Memory

8.9 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi 1.5 9B Chat on RTX 4000 Ada Laptop 12GB
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: 57.4 tok/s decode · 3.4s TTFT (warm) · 144 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	57.4 tok/s	1838 ms	62K
Coding	B	Runs well	57.4 tok/s	3370 ms	62K
Agentic Coding	C	Tight fit	57.4 tok/s	4902 ms	62K
Reasoning	B	Runs well	57.4 tok/s	3983 ms	62K
RAG	C	Tight fit	57.4 tok/s	6128 ms	62K

Inference speed

Yi 1.5 9B Chat inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Yi 1.5 9B Chat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	125.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	119.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	111.3	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	101.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	84.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	80.1	Fits
RTX 4070 12GB	12 GB	Q4_K_M	68.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	68.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	68.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	43.7	Fits
RTX 3060 12GB	12 GB	Q4_K_M	43.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	40.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	35.2	Fits
RTX 4060 8GB	8 GB	Q4_K_M	23.4	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Yi 1.5 9B Chat (9B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4	5.0 GB	Medium	C52
Q4_K_M	4	5.5 GB	Medium	C52
Q5_K_M	5	6.5 GB	High	C52
Q6_KBest for your GPU	6	7.4 GB	High	C52
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Get started

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

Run

lms load hf-bartowski--yi-1-5-9b-chat-gguf && lms server start

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run Yi 1.5 9B Chat?

Yes, RTX 4000 Ada Laptop 12GB can run Yi 1.5 9B Chat with a B grade (Runs well). Expected decode speed: 57.4 tok/s.

How much VRAM does Yi 1.5 9B Chat need?

Yi 1.5 9B Chat (9B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 9B Chat?

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

What speed will Yi 1.5 9B Chat run at on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Yi 1.5 9B Chat achieves approximately 57.4 tokens per second decode speed with a time-to-first-token of 3370ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run Yi 1.5 9B Chat for coding?

For coding workloads, Yi 1.5 9B Chat on RTX 4000 Ada Laptop 12GB receives a B grade with 57.4 tok/s and 62K context.

What context window can Yi 1.5 9B Chat use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Yi 1.5 9B Chat can safely use up to 62K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada Laptop 12GB See all hardware for Yi 1.5 9B Chat

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