Can Yi 1.5 6B run on GTX 1650 4GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Yi 1.5 6B needs ~6.2 GB but GTX 1650 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: Memory capacity

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) — 6.2 GB, exceeds 4.0 GB available

6.2 GB required4.0 GB available

155% VRAM needed

2.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.2 tok/s

TTFT

37015 ms

Safe context

Memory

6.2 GB / 4.0 GB

Offload

40%

Memory breakdown

Weights3.7 GB

KV Cache1.0 GB

Runtime1.2 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsYi 1.5 6B on GTX 1650 4GB
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: 5.2 tok/s decode · 37.0s TTFT (warm) · 13 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 6.2 GB, but this setup only exposes 4.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	5.8 tok/s	18358 ms	4K
Coding	F	Too heavy	4.8 tok/s	40254 ms	4K
Agentic Coding	F	Too heavy	3.5 tok/s	80589 ms	4K
Reasoning	F	Too heavy	4.8 tok/s	47573 ms	4K
RAG	F	Too heavy	3.5 tok/s	100736 ms	4K

Quantization options

How Yi 1.5 6B (6B params) fits at each quantization level on GTX 1650 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	F0
Q3_K_S	3	2.9 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Yi 1.5 6B well

GTX 1660 Super 6GBBudget pick

6 GB VRAM (+2)336 GB/s (+208)

Makes the model fit on the accelerator instead of staying completely out of reach.34.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 560%.

~$229 MSRP

RTX 3050 8GBBest value

8 GB VRAM (+4)224 GB/s (+96)

Makes the model fit on the accelerator instead of staying completely out of reach.43.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

GTX 1060 6GBNVIDIA upgrade

6 GB VRAM (+2)192 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.21 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 304%.

~$249 MSRP

Frequently asked questions

Can GTX 1650 4GB run Yi 1.5 6B?

No, Yi 1.5 6B requires more memory than GTX 1650 4GB provides.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 6.2 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 GTX 1650 4GB?

On GTX 1650 4GB, Yi 1.5 6B achieves approximately 4.8 tokens per second decode speed with a time-to-first-token of 40254ms using Q4_K_M quantization.

Can GTX 1650 4GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on GTX 1650 4GB receives a F grade with 4.8 tok/s and 4K context.

What context window can Yi 1.5 6B use on GTX 1650 4GB?

On GTX 1650 4GB, 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.

What should I upgrade first if Yi 1.5 6B feels slow on GTX 1650 4GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1650 4GB 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-gtx-1650-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: