Can Yi 1.5 6B run on RTX 4000 Ada 20GB?

YES — Runs Great

C50Usable

Estimated from fit model

Yi 1.5 6B needs ~7.8 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~83 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) — 7.8 GB, 83.4 tok/s, Runs well

7.8 GB required20.0 GB available

39% VRAM used

Fit status

Runs well

Decode

83.4 tok/s

TTFT

2320 ms

Safe context

Memory

7.8 GB / 20.0 GB

Memory breakdown

Weights3.7 GB

KV Cache1.0 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsYi 1.5 6B on RTX 4000 Ada 20GB
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: 83.4 tok/s decode · 2.3s TTFT (warm) · 209 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	83.4 tok/s	1266 ms	4K
Coding	C	Runs well	83.4 tok/s	2320 ms	4K
Agentic Coding	C	Runs well	83.4 tok/s	3375 ms	4K
Reasoning	C	Runs well	83.4 tok/s	2742 ms	4K
RAG	C	Runs well	83.4 tok/s	4219 ms	4K

Quantization options

How Yi 1.5 6B (6B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	C45
Q3_K_S	3	2.9 GB	Low	C45
NVFP4	4	3.4 GB	Medium	C46
Q4_K_M	4	3.7 GB	Medium	C46
Q5_K_M	5	4.3 GB	High	C46
Q6_K	6	4.9 GB	High	C47
Q8_0	8	6.4 GB	Very High	C48
F16Best for your GPU	16	12.3 GB	Maximum	C50

Get started

Copy-paste commands to run Yi 1.5 6B on your machine.

Run

lms load Yi-1.5-6B-Chat && lms server start

Frequently asked questions

Can RTX 4000 Ada 20GB run Yi 1.5 6B?

Yes, RTX 4000 Ada 20GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 83.4 tok/s.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 6B?

The recommended quantization for Yi 1.5 6B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 6B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Yi 1.5 6B achieves approximately 83.4 tokens per second decode speed with a time-to-first-token of 2320ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on RTX 4000 Ada 20GB receives a C grade with 83.4 tok/s and 4K context.

What context window can Yi 1.5 6B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Yi 1.5 6B 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 4000 Ada 20GB See all hardware for Yi 1.5 6B

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<iframe src="https://willitrunai.com/embed/yi-1.5-6b-on-rtx-4000-ada-20gb" 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

83.4 tok/s

1266 ms

Coding

Runs well

83.4 tok/s

2320 ms

Agentic Coding

Runs well

83.4 tok/s

3375 ms

Reasoning

Runs well

83.4 tok/s

2742 ms

RAG

Runs well

83.4 tok/s

4219 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.3 GB

Low

C45

Q3_K_S

2.9 GB

Low

C45

NVFP4

3.4 GB

Medium

C46

Q4_K_M

3.7 GB

Medium

C46

Q5_K_M

4.3 GB

High

C46

Q6_K

4.9 GB

High

C47

Q8_0

6.4 GB

Very High

C48

F16Best for your GPU

12.3 GB

Maximum

C50

Frequently asked questions

Can RTX 4000 Ada 20GB run Yi 1.5 6B?

Yes, RTX 4000 Ada 20GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 83.4 tok/s.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 6B?

The recommended quantization for Yi 1.5 6B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 6B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Yi 1.5 6B achieves approximately 83.4 tokens per second decode speed with a time-to-first-token of 2320ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on RTX 4000 Ada 20GB receives a C grade with 83.4 tok/s and 4K context.

What context window can Yi 1.5 6B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Yi 1.5 6B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.