Can Yi 1.5 6B run on RTX 2070 8GB?

YES — Runs Great

B56Good

Estimated from fit model

Yi 1.5 6B needs ~6.3 GB VRAM. RTX 2070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~80 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 6.3 GB, 79.9 tok/s, Runs well

6.3 GB required8.0 GB available

79% VRAM used

Fit status

Runs well

Decode

79.9 tok/s

TTFT

2424 ms

Safe context

Memory

6.3 GB / 8.0 GB

Memory breakdown

Weights3.7 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsYi 1.5 6B on RTX 2070 8GB
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: 79.9 tok/s decode · 2.4s TTFT (warm) · 200 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	79.9 tok/s	1322 ms	4K
Coding	B	Runs well	79.9 tok/s	2424 ms	4K
Agentic Coding	C	Tight fit	79.9 tok/s	3526 ms	4K
Reasoning	B	Runs well	79.9 tok/s	2865 ms	4K
RAG	C	Tight fit	79.9 tok/s	4407 ms	4K

Quantization options

How Yi 1.5 6B (6B params) fits at each quantization level on RTX 2070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	C52
Q3_K_S	3	2.9 GB	Low	C53
NVFP4	4	3.4 GB	Medium	C53
Q4_K_M	4	3.7 GB	Medium	C53
Q5_K_M	5	4.3 GB	High	C53
Q6_KBest for your GPU	6	4.9 GB	High	C53
Q8_0	8	6.4 GB	Very High	F0
F16	16	12.3 GB	Maximum	F0

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 2070 8GB run Yi 1.5 6B?

Yes, RTX 2070 8GB can run Yi 1.5 6B with a B grade (Runs well). Expected decode speed: 79.9 tok/s.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 6.3 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 2070 8GB?

On RTX 2070 8GB, Yi 1.5 6B achieves approximately 79.9 tokens per second decode speed with a time-to-first-token of 2424ms using Q4_K_M quantization.

Can RTX 2070 8GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on RTX 2070 8GB receives a B grade with 79.9 tok/s and 4K context.

What context window can Yi 1.5 6B use on RTX 2070 8GB?

On RTX 2070 8GB, 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 2070 8GB See all hardware for Yi 1.5 6B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/yi-1.5-6b-on-rtx-2070-8gb" 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

79.9 tok/s

1322 ms

Coding

Runs well

79.9 tok/s

2424 ms

Agentic Coding

Tight fit

79.9 tok/s

3526 ms

Reasoning

Runs well

79.9 tok/s

2865 ms

RAG

Tight fit

79.9 tok/s

4407 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.3 GB

Low

C52

Q3_K_S

2.9 GB

Low

C53

NVFP4

3.4 GB

Medium

C53

Q4_K_M

3.7 GB

Medium

C53

Q5_K_M

4.3 GB

High

C53

Q6_KBest for your GPU

4.9 GB

High

C53

Q8_0

6.4 GB

Very High

F16

12.3 GB

Maximum

Frequently asked questions

Can RTX 2070 8GB run Yi 1.5 6B?

Yes, RTX 2070 8GB can run Yi 1.5 6B with a B grade (Runs well). Expected decode speed: 79.9 tok/s.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 6.3 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 2070 8GB?

On RTX 2070 8GB, Yi 1.5 6B achieves approximately 79.9 tokens per second decode speed with a time-to-first-token of 2424ms using Q4_K_M quantization.

Can RTX 2070 8GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on RTX 2070 8GB receives a B grade with 79.9 tok/s and 4K context.

What context window can Yi 1.5 6B use on RTX 2070 8GB?

On RTX 2070 8GB, 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.