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

YES — With Q2_K

D38Poor

Estimated from fit model

Yi 1.5 6B Chat needs ~4.6 GB VRAM. GTX 1650 4GB has 4.0 GB. With Q2_K quantization, expect ~12 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Host offload

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.

Yi 1.5 6B Chat at Q4_K_M needs 6.0 GB — too much for GTX 1650 4GB (4.0 GB). Runs at Q2_K (4.6 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 6.0 GB, exceeds 4.0 GB available

6.0 GB required4.0 GB available

150% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.3 tok/s

TTFT

36480 ms

Safe context

Memory

6.0 GB / 4.0 GB

Offload

30%

Memory breakdown

Weights3.7 GB

KV Cache0.7 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 Chat 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: 5.3 tok/s decode · 36.5s TTFT (warm) · 13 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	6.1 tok/s	17412 ms	4K
Coding	F	Too heavy	5.3 tok/s	36480 ms	4K
Agentic Coding	F	Too heavy	4.2 tok/s	67777 ms	4K
Reasoning	F	Too heavy	5.3 tok/s	43112 ms	4K
RAG	F	Too heavy	4.2 tok/s	84721 ms	4K

Quantization options

How Yi 1.5 6B Chat (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	3.4 GB	Medium	F0
Q4_K_M	4	3.7 GB	Medium	F0
Q5_K_M	5	4.3 GB	High	F0
Q6_K	6	4.9 GB	High	F0
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 Chat on your machine.

Run

lms load hf-maziyarpanahi--yi-1-5-6b-chat-gguf && lms server start

Upgrade-Optionen

Hardware, die Yi 1.5 6B Chat gut ausführt

GTX 1660 Super 6GBGünstige Wahl

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

Makes the model fit on the accelerator instead of staying completely out of reach.34.7 tok/s Dekodierung

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.

ca. $229 MSRP

RTX 3050 8GBBestes Preis-Leistungs-Verhältnis

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

Makes the model fit on the accelerator instead of staying completely out of reach.40.4 tok/s Dekodierung

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.

ca. $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.21.3 tok/s Dekodierung

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.

ca. $249 MSRP

Frequently asked questions

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

Yes, GTX 1650 4GB can run Yi 1.5 6B Chat at Q2_K quantization (Very compromised (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 6.0 GB which exceeds available memory, but at Q2_K it needs only 4.6 GB. Expected decode speed: 12.2 tok/s.

How much VRAM does Yi 1.5 6B Chat need?

Yi 1.5 6B Chat (6B parameters) requires approximately 6.0 GB at Q4_K_M quantization. On GTX 1650 4GB, it fits at Q2_K using 4.6 GB.

What is the best quantization for Yi 1.5 6B Chat?

The recommended quantization is Q4_K_M, but on GTX 1650 4GB the best fitting quantization is Q2_K, which uses 4.6 GB.

What speed will Yi 1.5 6B Chat run at on GTX 1650 4GB?

On GTX 1650 4GB, Yi 1.5 6B Chat achieves approximately 12.2 tokens per second decode speed with a time-to-first-token of 15839ms using Q2_K quantization.

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

For coding workloads, Yi 1.5 6B Chat on GTX 1650 4GB receives a F grade with 5.3 tok/s and 4K context.

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

On GTX 1650 4GB, Yi 1.5 6B Chat can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

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

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for GTX 1650 4GB See all hardware for Yi 1.5 6B Chat

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-1-5-6b-chat-gguf-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: