Can Yi 1.5 9B run on RX 5600 XT 6GB?

YES — With Q2_K

C44Usable

Estimated from fit model

Yi 1.5 9B needs ~6.5 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With Q2_K quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

Yi 1.5 9B at Q4_K_M needs 8.5 GB — too much for RX 5600 XT 6GB (6.0 GB). Runs at Q2_K (6.5 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.5 GB, exceeds 6.0 GB available

8.5 GB required6.0 GB available

142% VRAM needed

2.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.8 tok/s

TTFT

17912 ms

Safe context

Memory

8.5 GB / 6.0 GB

Offload

30%

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsYi 1.5 9B on RX 5600 XT 6GB
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: 10.8 tok/s decode · 17.9s TTFT (warm) · 27 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 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	13.1 tok/s	8074 ms	4K
Coding	F	Too heavy	9.9 tok/s	19479 ms	4K
Agentic Coding	F	Too heavy	7.7 tok/s	36470 ms	4K
Reasoning	F	Too heavy	10.8 tok/s	21169 ms	4K
RAG	F	Too heavy	7.7 tok/s	45588 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.5 GB	Low	B58
Q3_K_S	3	4.4 GB	Low	F0
NVFP4	4	5.0 GB	Medium	F0
Q4_K_M	4	5.5 GB	Medium	F0
Q5_K_M	5	6.5 GB	High	F0
Q6_K	6	7.4 GB	High	F0
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

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-Optionen

Hardware, die Yi 1.5 9B gut ausführt

RX 580 8GBBestes Preis-Leistungs-Verhältnis

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.13.4 tok/s Dekodierung

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

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

ca. $229 MSRP

RX 9060 8GBAMD-Upgrade

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.22.8 tok/s Dekodierung

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

Raises estimated decode speed by about 111%.

ca. $249 MSRP

RX 7600 XT 16GBGünstige Wahl

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.33.1 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. $329 MSRP

RTX 3080 Ti 12GBGrößter Sprung

12 GB VRAM (+6)912 GB/s (+624)

Makes the model fit on the accelerator instead of staying completely out of reach.108 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. $1,199 MSRP

Frequently asked questions

Can RX 5600 XT 6GB run Yi 1.5 9B?

Yes, RX 5600 XT 6GB can run Yi 1.5 9B at Q2_K quantization (Runs with offload (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 8.5 GB which exceeds available memory, but at Q2_K it needs only 6.5 GB. Expected decode speed: 25.2 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 8.5 GB at Q4_K_M quantization. On RX 5600 XT 6GB, it fits at Q2_K using 6.5 GB.

What is the best quantization for Yi 1.5 9B?

The recommended quantization is Q4_K_M, but on RX 5600 XT 6GB the best fitting quantization is Q2_K, which uses 6.5 GB.

What speed will Yi 1.5 9B run at on RX 5600 XT 6GB?

On RX 5600 XT 6GB, Yi 1.5 9B achieves approximately 25.2 tokens per second decode speed with a time-to-first-token of 7683ms using Q2_K quantization.

Can RX 5600 XT 6GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on RX 5600 XT 6GB receives a F grade with 9.9 tok/s and 4K context.

What context window can Yi 1.5 9B use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, Yi 1.5 9B can safely use up to 4K tokens of context at Q2_K quantization. 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 RX 5600 XT 6GB?

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 RX 5600 XT 6GB See all hardware for Yi 1.5 9B

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