Can Yi 1.5 6B run on RX 580 8GB?

YES — Runs Great

C53Usable

Estimated from fit model

Yi 1.5 6B needs ~6.3 GB VRAM. RX 580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~33 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) — 6.3 GB, 32.7 tok/s, Runs well

6.3 GB required8.0 GB available

79% VRAM used

Fit status

Runs well

Decode

32.7 tok/s

TTFT

5919 ms

Safe context

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 RX 580 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: 32.7 tok/s decode · 5.9s TTFT (warm) · 82 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	32.7 tok/s	3229 ms	4K
Coding	C	Runs well	32.7 tok/s	5919 ms	4K
Agentic Coding	C	Tight fit	32.7 tok/s	8609 ms	4K
Reasoning	C	Runs well	32.7 tok/s	6995 ms	4K
RAG	C	Tight fit	32.7 tok/s	10762 ms	4K

Quantization options

How Yi 1.5 6B (6B params) fits at each quantization level on RX 580 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	C52
Q3_K_S	3	2.9 GB	Low	C53
NVFP4	4	3.4 GB	Medium	C53
Q4_K_M	4	3.7 GB	Medium	C53
Q5_K_M	5	4.3 GB	High	C53
Q6_KBest for your GPU	6	4.9 GB	High	C53
Q8_0	8	6.4 GB	Very High	F0
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

Opciones de mejora

Hardware que ejecuta bien Yi 1.5 6B

Intel Arc B570 10GBOpción económica

10 GB VRAM (+2)380 GB/s (+124)

Sube la velocidad estimada de decodificación alrededor de un 87%.61 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 87%.

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

~$219 MSRP

RTX 5070 12GBMejor relación calidad-precio

12 GB VRAM (+4)672 GB/s (+416)

Sube la velocidad estimada de decodificación alrededor de un 249%.114 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 249%.

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

~$549 MSRP

Frequently asked questions

Can RX 580 8GB run Yi 1.5 6B?

Yes, RX 580 8GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 32.7 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 RX 580 8GB?

On RX 580 8GB, Yi 1.5 6B achieves approximately 32.7 tokens per second decode speed with a time-to-first-token of 5919ms using Q4_K_M quantization.

Can RX 580 8GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on RX 580 8GB receives a C grade with 32.7 tok/s and 4K context.

What context window can Yi 1.5 6B use on RX 580 8GB?

On RX 580 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 RX 580 8GB See all hardware for Yi 1.5 6B

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