Can Yi 1.5 9B Chat run on RX 6600 8GB?

Q: What should I upgrade first if Yi 1.5 9B Chat feels slow on RX 6600 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C48Usable

Estimated from fit model

Yi 1.5 9B Chat needs ~8.2 GB VRAM. RX 6600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~14 tok/s.

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

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, 14.1 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

14.1 tok/s

TTFT

13757 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 RX 6600 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: 14.1 tok/s decode · 13.8s TTFT (warm) · 35 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

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	20.0 tok/s	5282 ms	12K
Coding	C	Runs with offload (needs ~0.2 GB host RAM)	14.1 tok/s	13757 ms	12K
Agentic Coding	D	Very compromised (needs ~0.8 GB host RAM)	10.9 tok/s	25780 ms	12K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	14.1 tok/s	16258 ms	12K
RAG	D	Very compromised (needs ~0.8 GB host RAM)	10.9 tok/s

Inference speed

Yi 1.5 9B Chat inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Yi 1.5 9B Chat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits

Quantization options

How Yi 1.5 9B Chat (9B params) fits at each quantization level on RX 6600 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

RX 7600 XT 16GBBudget pick

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

Raises estimated decode speed by about 116%.30.4 tok/s decode

Raises estimated decode speed by about 116%.

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

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+8)320 GB/s (+96)

Raises estimated decode speed by about 160%.36.7 tok/s decode

Raises estimated decode speed by about 160%.

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

~$349 MSRP

RX 7700 XT 12GBAMD upgrade

12 GB VRAM (+4)432 GB/s (+208)

Raises estimated decode speed by about 235%.47.2 tok/s decode

Raises estimated decode speed by about 235%.

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

~$449 MSRP

Frequently asked questions

Can RX 6600 8GB run Yi 1.5 9B Chat?

Yes, RX 6600 8GB can run Yi 1.5 9B Chat with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 14.1 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 RX 6600 8GB?

On RX 6600 8GB, Yi 1.5 9B Chat achieves approximately 14.1 tokens per second decode speed with a time-to-first-token of 13757ms using Q4_K_M quantization.

Can RX 6600 8GB run Yi 1.5 9B Chat for coding?

For coding workloads, Yi 1.5 9B Chat on RX 6600 8GB receives a C grade with 14.1 tok/s and 12K context.

What context window can Yi 1.5 9B Chat use on RX 6600 8GB?

On RX 6600 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 RX 6600 8GB?

See all results for RX 6600 8GB See all hardware for Yi 1.5 9B Chat

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