Will It Run AI

Can Starling LM 7B run on NVIDIA L4 24GB?

YES — Runs Great

C49Usable
Estimated from fit model

Starling LM 7B needs ~9.8 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 9.8 GB, 49.1 tok/s, Runs well
9.8 GB required24.0 GB available
41% VRAM used

Fit status

Runs well

Decode

49.1 tok/s

TTFT

3944 ms

Safe context

8K

Memory

9.8 GB / 24.0 GB

Memory breakdown

Weights4.3 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsStarling LM 7B on NVIDIA L4 24GB
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: 49.1 tok/s decode · 3.9s TTFT (warm) · 123 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 well49.1 tok/s2151 ms8K
CodingCRuns well49.1 tok/s3944 ms8K
Agentic CodingCRuns well49.1 tok/s5736 ms8K
ReasoningCRuns well49.1 tok/s4661 ms8K
RAGCRuns well49.1 tok/s7170 ms8K

Quantization options

How Starling LM 7B (7B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC45
Q3_K_S
3
3.4 GB
LowC45
NVFP4
4
3.9 GB
MediumC45
Q4_K_M
4
4.3 GB
MediumC45
Q5_K_M
5
5.0 GB
HighC46
Q6_K
6
5.7 GB
HighC46
Q8_0
8
7.5 GB
Very HighC47
F16Best for your GPU
16
14.3 GB
MaximumC50

Get started

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

Run

ollama run starling-lm

Upgrade-Optionen

Hardware, die Starling LM 7B gut ausführt

NVIDIARTX 5090 32GBGünstige Wahl
32 GB VRAM (+8)1792 GB/s (+1492)
C
Raises estimated decode speed by about 100%.98 tok/s Dekodierung

Raises estimated decode speed by about 100%.

Adds memory headroom for longer context windows and future model growth.

ca. $1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis
32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 100%.98 tok/s Dekodierung

Raises estimated decode speed by about 100%.

Adds memory headroom for longer context windows and future model growth.

ca. $2,499 MSRP

NVIDIARTX 5000 Ada 32GBNVIDIA-Upgrade
32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 100%.98 tok/s Dekodierung

Raises estimated decode speed by about 100%.

Adds memory headroom for longer context windows and future model growth.

ca. $4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Starling LM 7B?

Yes, NVIDIA L4 24GB can run Starling LM 7B with a C grade (Runs well). Expected decode speed: 49.1 tok/s.

How much VRAM does Starling LM 7B need?

Starling LM 7B (7B parameters) requires approximately 9.8 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Starling LM 7B achieves approximately 49.1 tokens per second decode speed with a time-to-first-token of 3944ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Starling LM 7B for coding?

For coding workloads, Starling LM 7B on NVIDIA L4 24GB receives a C grade with 49.1 tok/s and 8K context.

What context window can Starling LM 7B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, 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 NVIDIA L4 24GBSee all hardware for Starling LM 7B
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