Would CUDA be a better path than Intel Arc A750 8GB for Yi 1.5 9B Chat?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can Yi 1.5 9B Chat run on Intel Arc A750 8GB?

Q: What should I upgrade first if Yi 1.5 9B Chat feels slow on Intel Arc A750 8GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

YES — With Offload

C50Usable

Estimated from fit model

Yi 1.5 9B Chat needs ~8.2 GB VRAM. Intel Arc A750 8GB has 8.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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) — 8.2 GB, 28.2 tok/s, Runs with offload (needs ~0.2 GB host RAM)

8.2 GB required8.0 GB available

102% VRAM needed

0.2 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

28.2 tok/s

TTFT

6858 ms

Safe context

12K

Memory

8.2 GB / 8.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsYi 1.5 9B Chat on Intel Arc A750 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: 28.2 tok/s decode · 6.9s TTFT (warm) · 71 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	40.1 tok/s	2633 ms	12K
Coding	C	Runs with offload (needs ~0.2 GB host RAM)	28.2 tok/s	6858 ms	12K
Agentic Coding	D	Very compromised (needs ~0.8 GB host RAM)	21.9 tok/s	12853 ms	12K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	28.2 tok/s	8105 ms	12K
RAG	D	Very compromised (needs ~0.8 GB host RAM)	21.9 tok/s

Quantization options

How Yi 1.5 9B Chat (9B params) fits at each quantization level on Intel Arc A750 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C53
Q3_K_S	3	4.4 GB	Low	C53
NVFP4Best for your GPU

Get started

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

Run

lms load hf-bartowski--yi-1-5-9b-chat-gguf && lms server start

Upgrade options

Hardware that runs Yi 1.5 9B Chat well

Intel Arc B570 10GBBudget pick

10 GB VRAM (+2)

Raises estimated decode speed by about 33%.37.4 tok/s decode

Raises estimated decode speed by about 33%.

Adds memory headroom for longer context windows and future model growth.

~$219 MSRP

Intel Arc B580 12GBBest value

12 GB VRAM (+4)

Raises estimated decode speed by about 41%.39.9 tok/s decode

Raises estimated decode speed by about 41%.

Adds memory headroom for longer context windows and future model growth.

~$249 MSRP

Intel Arc A770 16GBIntel upgrade

16 GB VRAM (+8)560 GB/s (+48)

Raises estimated decode speed by about 63%.45.9 tok/s decode

Raises estimated decode speed by about 63%.

Adds memory headroom for longer context windows and future model growth.

~$349 MSRP

Frequently asked questions

Can Intel Arc A750 8GB run Yi 1.5 9B Chat?

Yes, Intel Arc A750 8GB can run Yi 1.5 9B Chat with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 28.2 tok/s.

How much VRAM does Yi 1.5 9B Chat need?

Yi 1.5 9B Chat (9B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 9B Chat?

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

What speed will Yi 1.5 9B Chat run at on Intel Arc A750 8GB?

On Intel Arc A750 8GB, Yi 1.5 9B Chat achieves approximately 28.2 tokens per second decode speed with a time-to-first-token of 6858ms using Q4_K_M quantization.

Can Intel Arc A750 8GB run Yi 1.5 9B Chat for coding?

For coding workloads, Yi 1.5 9B Chat on Intel Arc A750 8GB receives a C grade with 28.2 tok/s and 12K context.

What context window can Yi 1.5 9B Chat use on Intel Arc A750 8GB?

On Intel Arc A750 8GB, Yi 1.5 9B Chat can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Yi 1.5 9B Chat feels slow on Intel Arc A750 8GB?

See all results for Intel Arc A750 8GB See all hardware for Yi 1.5 9B Chat

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