Can Yi 1.5 6B run on RX 7600 XT 16GB?

YES — Runs Great

C49Usable

Estimated from fit model

Yi 1.5 6B needs ~7.1 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~50 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 7.1 GB, 49.6 tok/s, Runs well

7.1 GB required16.0 GB available

44% VRAM used

Fit status

Runs well

Decode

49.6 tok/s

TTFT

3901 ms

Safe context

Memory

7.1 GB / 16.0 GB

Memory breakdown

Weights3.7 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsYi 1.5 6B on RX 7600 XT 16GB
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: 49.6 tok/s decode · 3.9s TTFT (warm) · 124 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	49.6 tok/s	2128 ms	4K
Coding	C	Runs well	49.6 tok/s	3901 ms	4K
Agentic Coding	C	Runs well	49.6 tok/s	5674 ms	4K
Reasoning	C	Runs well	49.6 tok/s	4610 ms	4K
RAG	C	Runs well	49.6 tok/s	7092 ms	4K

Quantization options

How Yi 1.5 6B (6B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	C46
Q3_K_S	3	2.9 GB	Low	C47
NVFP4	4	3.4 GB	Medium	C47
Q4_K_M	4	3.7 GB	Medium	C47
Q5_K_M	5	4.3 GB	High	C48
Q6_K	6	4.9 GB	High	C48
Q8_0	8	6.4 GB	Very High	C50
F16Best for your GPU	16	12.3 GB	Maximum	C50

Get started

Copy-paste commands to run Yi 1.5 6B on your machine.

Run

lms load Yi-1.5-6B-Chat && lms server start

Opções de upgrade

Hardware que roda bem Yi 1.5 6B

RX 7900 XT 20GBOpção econômica

20 GB VRAM (+4)800 GB/s (+512)

Raises estimated decode speed by about 69%.84 tok/s decodificação

Raises estimated decode speed by about 69%.

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

~$899 MSRP

RTX 4000 Ada 20GBMelhor custo-benefício

20 GB VRAM (+4)360 GB/s (+72)

Raises estimated decode speed by about 68%.83.4 tok/s decodificação

Raises estimated decode speed by about 68%.

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

~$1,250 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run Yi 1.5 6B?

Yes, RX 7600 XT 16GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 49.6 tok/s.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 6B?

The recommended quantization for Yi 1.5 6B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 6B run at on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Yi 1.5 6B achieves approximately 49.6 tokens per second decode speed with a time-to-first-token of 3901ms using Q4_K_M quantization.

Can RX 7600 XT 16GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on RX 7600 XT 16GB receives a C grade with 49.6 tok/s and 4K context.

What context window can Yi 1.5 6B use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Yi 1.5 6B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for RX 7600 XT 16GB See all hardware for Yi 1.5 6B

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