Can Starling LM 7B run on RTX A4000 16GB?

YES — Runs Great

C54Usable

Estimated from fit model

Starling LM 7B needs ~9.0 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~79 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 9.0 GB, 79.0 tok/s, Runs well

9.0 GB required16.0 GB available

56% VRAM used

Fit status

Runs well

Decode

79.0 tok/s

TTFT

2452 ms

Safe context

Memory

9.0 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsStarling LM 7B on RTX A4000 16GB
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: 79.0 tok/s decode · 2.5s TTFT (warm) · 197 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	79.0 tok/s	1338 ms	8K
Coding	C	Runs well	79.0 tok/s	2452 ms	8K
Agentic Coding	B	Runs well	79.0 tok/s	3567 ms	8K
Reasoning	C	Runs well	79.0 tok/s	2898 ms	8K
RAG	B	Runs well	79.0 tok/s	4458 ms	8K

Quantization options

How Starling LM 7B (7B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C47
Q3_K_S	3	3.4 GB	Low	C48
NVFP4	4	3.9 GB	Medium	C48
Q4_K_M	4	4.3 GB	Medium	C48
Q5_K_M	5	5.0 GB	High	C49
Q6_K	6	5.7 GB	High	C50
Q8_0Best for your GPU	8	7.5 GB	Very High	C52
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run Starling LM 7B on your machine.

Run

ollama run starling-lm

Frequently asked questions

Can RTX A4000 16GB run Starling LM 7B?

Yes, RTX A4000 16GB can run Starling LM 7B with a C grade (Runs well). Expected decode speed: 79.0 tok/s.

How much VRAM does Starling LM 7B need?

Starling LM 7B (7B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Starling LM 7B?

The recommended quantization for Starling LM 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Starling LM 7B run at on RTX A4000 16GB?

On RTX A4000 16GB, Starling LM 7B achieves approximately 79.0 tokens per second decode speed with a time-to-first-token of 2452ms using Q4_K_M quantization.

Can RTX A4000 16GB run Starling LM 7B for coding?

For coding workloads, Starling LM 7B on RTX A4000 16GB receives a C grade with 79.0 tok/s and 8K context.

What context window can Starling LM 7B use on RTX A4000 16GB?

On RTX A4000 16GB, Starling LM 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RTX A4000 16GB See all hardware for Starling LM 7B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/starling-7b-on-a4000-16gb" 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

79.0 tok/s

1338 ms

Coding

Runs well

79.0 tok/s

2452 ms

Agentic Coding

Runs well

79.0 tok/s

3567 ms

Reasoning

Runs well

79.0 tok/s

2898 ms

RAG

Runs well

79.0 tok/s

4458 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

C47

Q3_K_S

3.4 GB

Low

C48

NVFP4

3.9 GB

Medium

C48

Q4_K_M

4.3 GB

Medium

C48

Q5_K_M

5.0 GB

High

C49

Q6_K

5.7 GB

High

C50

Q8_0Best for your GPU

7.5 GB

Very High

C52

F16

14.3 GB

Maximum

Frequently asked questions

Can RTX A4000 16GB run Starling LM 7B?

Yes, RTX A4000 16GB can run Starling LM 7B with a C grade (Runs well). Expected decode speed: 79.0 tok/s.

How much VRAM does Starling LM 7B need?

Starling LM 7B (7B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Starling LM 7B?

The recommended quantization for Starling LM 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Starling LM 7B run at on RTX A4000 16GB?

On RTX A4000 16GB, Starling LM 7B achieves approximately 79.0 tokens per second decode speed with a time-to-first-token of 2452ms using Q4_K_M quantization.

Can RTX A4000 16GB run Starling LM 7B for coding?

For coding workloads, Starling LM 7B on RTX A4000 16GB receives a C grade with 79.0 tok/s and 8K context.

What context window can Starling LM 7B use on RTX A4000 16GB?

On RTX A4000 16GB, Starling LM 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.