Can Yi 1.5 9B run on RTX A4500 20GB?

YES — Runs Great

B57Good

Estimated from fit model

Yi 1.5 9B needs ~10.2 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~99 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 10.2 GB, 98.9 tok/s, Runs well

10.2 GB required20.0 GB available

51% VRAM used

Fit status

Runs well

Decode

98.9 tok/s

TTFT

1958 ms

Safe context

Memory

10.2 GB / 20.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on RTX A4500 20GB
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: 98.9 tok/s decode · 2.0s TTFT (warm) · 247 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	B	Runs well	98.9 tok/s	1068 ms	4K
Coding	B	Runs well	98.9 tok/s	1958 ms	4K
Agentic Coding	B	Runs well	98.9 tok/s	2848 ms	4K
Reasoning	B	Runs well	98.9 tok/s	2314 ms	4K
RAG	B	Runs well	98.9 tok/s	3560 ms	4K

Inference speed

Yi 1.5 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Yi 1.5 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	121.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	110.3	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	91.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	87.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	74.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	74.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	74.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.5	Fits
RTX 3060 12GB	12 GB	Q4_K_M	47.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.6	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	38.3	Fits
RTX 4060 8GB	8 GB	Q4_K_M	23.3	Heavy offload

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4	5.0 GB	Medium	C51
Q4_K_M	4	5.5 GB	Medium	C52
Q5_K_M	5	6.5 GB	High	C52
Q6_K	6	7.4 GB	High	C53
Q8_0Best for your GPU	8	9.6 GB	Very High	C55
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

Frequently asked questions

Can RTX A4500 20GB run Yi 1.5 9B?

Yes, RTX A4500 20GB can run Yi 1.5 9B with a B grade (Runs well). Expected decode speed: 98.9 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 10.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 9B?

The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 9B run at on RTX A4500 20GB?

On RTX A4500 20GB, Yi 1.5 9B achieves approximately 98.9 tokens per second decode speed with a time-to-first-token of 1958ms using Q4_K_M quantization.

Can RTX A4500 20GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on RTX A4500 20GB receives a B grade with 98.9 tok/s and 4K context.

What context window can Yi 1.5 9B use on RTX A4500 20GB?

On RTX A4500 20GB, Yi 1.5 9B 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 RTX A4500 20GB See all hardware for Yi 1.5 9B

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