Can Yi 1.5 9B run on NVIDIA A16 64GB?

YES — Runs Great

C51Usable

Estimated from fit model

Yi 1.5 9B needs ~14.6 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~93 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) — 14.6 GB, 92.7 tok/s, Runs well

14.6 GB required64.0 GB available

23% VRAM used

Fit status

Runs well

Decode

92.7 tok/s

TTFT

2088 ms

Safe context

Memory

14.6 GB / 64.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on NVIDIA A16 64GB
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: 92.7 tok/s decode · 2.1s TTFT (warm) · 232 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	92.7 tok/s	1139 ms	4K
Coding	C	Runs well	92.7 tok/s	2088 ms	4K
Agentic Coding	C	Runs well	92.7 tok/s	3038 ms	4K
Reasoning	C	Runs well	92.7 tok/s	2468 ms	4K
RAG	C	Runs well	92.7 tok/s	3797 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C45
Q3_K_S	3	4.4 GB	Low	C45
NVFP4	4	5.0 GB	Medium	C45
Q4_K_M	4	5.5 GB	Medium	C45
Q5_K_M	5	6.5 GB	High	C45
Q6_K	6	7.4 GB	High	C45
Q8_0	8	9.6 GB	Very High	C46
F16Best for your GPU	16	18.5 GB	Maximum	C47

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

Upgrade options

Hardware that runs Yi 1.5 9B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)

This setup is broadly balanced for this model.74.3 tok/s decode

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+32)819 GB/s (+219)

This setup is broadly balanced for this model.110.3 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Yi 1.5 9B?

Yes, NVIDIA A16 64GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 92.7 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 14.6 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 NVIDIA A16 64GB?

On NVIDIA A16 64GB, Yi 1.5 9B achieves approximately 92.7 tokens per second decode speed with a time-to-first-token of 2088ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on NVIDIA A16 64GB receives a C grade with 92.7 tok/s and 4K context.

What context window can Yi 1.5 9B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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 NVIDIA A16 64GB See all hardware for Yi 1.5 9B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/yi-1.5-9b-on-a16-64gb" 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

92.7 tok/s

1139 ms

Coding

Runs well

92.7 tok/s

2088 ms

Agentic Coding

Runs well

92.7 tok/s

3038 ms

Reasoning

Runs well

92.7 tok/s

2468 ms

RAG

Runs well

92.7 tok/s

3797 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

C45

Q3_K_S

4.4 GB

Low

C45

NVFP4

5.0 GB

Medium

C45

Q4_K_M

5.5 GB

Medium

C45

Q5_K_M

6.5 GB

High

C45

Q6_K

7.4 GB

High

C45

Q8_0

9.6 GB

Very High

C46

F16Best for your GPU

18.5 GB

Maximum

C47

Frequently asked questions

Can NVIDIA A16 64GB run Yi 1.5 9B?

Yes, NVIDIA A16 64GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 92.7 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 14.6 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 NVIDIA A16 64GB?

On NVIDIA A16 64GB, Yi 1.5 9B achieves approximately 92.7 tokens per second decode speed with a time-to-first-token of 2088ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on NVIDIA A16 64GB receives a C grade with 92.7 tok/s and 4K context.

What context window can Yi 1.5 9B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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.