Can Yi 1.5 6B run on RX 7700 XT 12GB?

YES — Runs Great

C53Usable

Estimated from fit model

Yi 1.5 6B needs ~6.7 GB VRAM. RX 7700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~77 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) — 6.7 GB, 77.0 tok/s, Runs well

6.7 GB required12.0 GB available

56% VRAM used

Fit status

Runs well

Decode

77.0 tok/s

TTFT

2514 ms

Safe context

Memory

6.7 GB / 12.0 GB

Memory breakdown

Weights3.7 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi 1.5 6B on RX 7700 XT 12GB
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: 77.0 tok/s decode · 2.5s TTFT (warm) · 193 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	77.0 tok/s	1371 ms	4K
Coding	C	Runs well	77.0 tok/s	2514 ms	4K
Agentic Coding	C	Runs well	77.0 tok/s	3656 ms	4K
Reasoning	C	Runs well	77.0 tok/s	2971 ms	4K
RAG	C	Runs well	77.0 tok/s	4570 ms	4K

Quantization options

How Yi 1.5 6B (6B params) fits at each quantization level on RX 7700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	C48
Q3_K_S	3	2.9 GB	Low	C49
NVFP4	4	3.4 GB	Medium	C50
Q4_K_M	4	3.7 GB	Medium	C50
Q5_K_M	5	4.3 GB	High	C51
Q6_K	6	4.9 GB	High	C52
Q8_0Best for your GPU	8	6.4 GB	Very High	C52
F16	16	12.3 GB	Maximum	F0

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

Frequently asked questions

Can RX 7700 XT 12GB run Yi 1.5 6B?

Yes, RX 7700 XT 12GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 77.0 tok/s.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 6.7 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 7700 XT 12GB?

On RX 7700 XT 12GB, Yi 1.5 6B achieves approximately 77.0 tokens per second decode speed with a time-to-first-token of 2514ms using Q4_K_M quantization.

Can RX 7700 XT 12GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on RX 7700 XT 12GB receives a C grade with 77.0 tok/s and 4K context.

What context window can Yi 1.5 6B use on RX 7700 XT 12GB?

On RX 7700 XT 12GB, 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 7700 XT 12GB See all hardware for Yi 1.5 6B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/yi-1.5-6b-on-rx-7700-xt-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

77.0 tok/s

1371 ms

Coding

Runs well

77.0 tok/s

2514 ms

Agentic Coding

Runs well

77.0 tok/s

3656 ms

Reasoning

Runs well

77.0 tok/s

2971 ms

RAG

Runs well

77.0 tok/s

4570 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.3 GB

Low

C48

Q3_K_S

2.9 GB

Low

C49

NVFP4

3.4 GB

Medium

C50

Q4_K_M

3.7 GB

Medium

C50

Q5_K_M

4.3 GB

High

C51

Q6_K

4.9 GB

High

C52

Q8_0Best for your GPU

6.4 GB

Very High

C52

F16

12.3 GB

Maximum

Frequently asked questions

Can RX 7700 XT 12GB run Yi 1.5 6B?

Yes, RX 7700 XT 12GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 77.0 tok/s.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 6.7 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 7700 XT 12GB?

On RX 7700 XT 12GB, Yi 1.5 6B achieves approximately 77.0 tokens per second decode speed with a time-to-first-token of 2514ms using Q4_K_M quantization.

Can RX 7700 XT 12GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on RX 7700 XT 12GB receives a C grade with 77.0 tok/s and 4K context.

What context window can Yi 1.5 6B use on RX 7700 XT 12GB?

On RX 7700 XT 12GB, 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.