Will It Run AI

Can Yi 1.5 6B run on Radeon RX 7600M 8GB?

YES — Runs Great

C55Usable
Estimated from fit model

Yi 1.5 6B needs ~6.3 GB VRAM. Radeon RX 7600M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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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) — 6.3 GB, 50.5 tok/s, Runs well
6.3 GB required8.0 GB available
79% VRAM used

Fit status

Runs well

Decode

50.5 tok/s

TTFT

3835 ms

Safe context

4K

Memory

6.3 GB / 8.0 GB

Memory breakdown

Weights3.7 GB
KV Cache1.0 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsYi 1.5 6B on Radeon RX 7600M 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: 50.5 tok/s decode · 3.8s TTFT (warm) · 126 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well50.5 tok/s2092 ms4K
CodingCRuns well50.5 tok/s3835 ms4K
Agentic CodingCTight fit50.5 tok/s5578 ms4K
ReasoningCRuns well50.5 tok/s4532 ms4K
RAGCTight fit50.5 tok/s6972 ms4K

Quantization options

How Yi 1.5 6B (6B params) fits at each quantization level on Radeon RX 7600M 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowC52
Q3_K_S
3
2.9 GB
LowC53
NVFP4
4
3.4 GB
MediumC53
Q4_K_M
4
3.7 GB
MediumC53
Q5_K_M
5
4.3 GB
HighC53
Q6_KBest for your GPU
6
4.9 GB
HighC53
Q8_0
8
6.4 GB
Very HighF0
F16
16
12.3 GB
MaximumF0

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

Opciones de mejora

Hardware que ejecuta bien Yi 1.5 6B

NVIDIARTX 3080 10GBOpción económica
10 GB VRAM (+2)760 GB/s (+472)
C
Sube la velocidad estimada de decodificación alrededor de un 43%.72 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 43%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$699 MSRP

Frequently asked questions

Can Radeon RX 7600M 8GB run Yi 1.5 6B?

Yes, Radeon RX 7600M 8GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 50.5 tok/s.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 6.3 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 Radeon RX 7600M 8GB?

On Radeon RX 7600M 8GB, Yi 1.5 6B achieves approximately 50.5 tokens per second decode speed with a time-to-first-token of 3835ms using Q4_K_M quantization.

Can Radeon RX 7600M 8GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on Radeon RX 7600M 8GB receives a C grade with 50.5 tok/s and 4K context.

What context window can Yi 1.5 6B use on Radeon RX 7600M 8GB?

On Radeon RX 7600M 8GB, 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 Radeon RX 7600M 8GBSee all hardware for Yi 1.5 6B
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