Can Yi 1.5 9B run on RTX 5050 8GB?

BARELY — Tight on Memory

C44Usable

Estimated from fit model

Yi 1.5 9B needs ~8.7 GB VRAM. RTX 5050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.7 GB, 24.3 tok/s, Very compromised (needs ~0.4 GB host RAM)

8.7 GB required8.0 GB available

109% VRAM needed

0.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

24.3 tok/s

TTFT

7975 ms

Safe context

Memory

8.7 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on RTX 5050 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: 24.3 tok/s decode · 8.0s TTFT (warm) · 61 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.

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 {ram} 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	B	Runs with offload	34.3 tok/s	3081 ms	4K
Coding	C	Very compromised	22.3 tok/s	8673 ms	4K
Agentic Coding	F	Too heavy	16.2 tok/s	17355 ms	4K
Reasoning	C	Very compromised	22.3 tok/s	10250 ms	4K
RAG	F	Too heavy	16.2 tok/s	21694 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on RTX 5050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B58
Q3_K_S	3	4.4 GB	Low	B58
NVFP4Best for your GPU

Get started

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

Run

lms load Yi-1.5-9B-Chat && lms server start

Upgrade options

Hardware that runs Yi 1.5 9B well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+136)

Removes host-memory offload, which is usually the single biggest latency and throughput win.39.5 tok/s decode

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

Raises estimated decode speed by about 63%.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+224)

Removes host-memory offload, which is usually the single biggest latency and throughput win.55 tok/s decode

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

Raises estimated decode speed by about 126%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)288 GB/s (+64)

Removes host-memory offload, which is usually the single biggest latency and throughput win.38.3 tok/s decode

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

Raises estimated decode speed by about 58%.

~$499 MSRP

Frequently asked questions

Can RTX 5050 8GB run Yi 1.5 9B?

Yes, RTX 5050 8GB can run Yi 1.5 9B with a C grade (Very compromised). Expected decode speed: 22.3 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 9B?

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

What speed will Yi 1.5 9B run at on RTX 5050 8GB?

On RTX 5050 8GB, Yi 1.5 9B achieves approximately 22.3 tokens per second decode speed with a time-to-first-token of 8673ms using Q4_K_M quantization.

Can RTX 5050 8GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on RTX 5050 8GB receives a C grade with 22.3 tok/s and 4K context.

What context window can Yi 1.5 9B use on RTX 5050 8GB?

On RTX 5050 8GB, Yi 1.5 9B 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 9B feels slow on RTX 5050 8GB?

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 RTX 5050 8GB See all hardware for Yi 1.5 9B

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