Will It Run AI

Can Starling LM 7B run on Tesla P40 24GB?

YES — Runs Great

C49Usable
Estimated from fit model

Starling LM 7B needs ~9.8 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~51 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, 51.4 tok/s, Runs well
9.8 GB required24.0 GB available
41% VRAM used

Fit status

Runs well

Decode

51.4 tok/s

TTFT

3767 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 Tesla P40 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: 51.4 tok/s decode · 3.8s TTFT (warm) · 129 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well51.4 tok/s2055 ms8K
CodingCRuns well51.4 tok/s3767 ms8K
Agentic CodingCRuns well51.4 tok/s5479 ms8K
ReasoningCRuns well51.4 tok/s4452 ms8K
RAGCRuns well51.4 tok/s6849 ms8K

Quantization options

How Starling LM 7B (7B params) fits at each quantization level on Tesla P40 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

Opciones de mejora

Hardware que ejecuta bien Starling LM 7B

NVIDIARTX 5090 32GBOpción económica
32 GB VRAM (+8)1792 GB/s (+1446)
C
Sube la velocidad estimada de decodificación alrededor de un 91%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 91%.

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

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBMejor relación calidad-precio
32 GB VRAM (+8)896 GB/s (+550)
C
Sube la velocidad estimada de decodificación alrededor de un 91%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 91%.

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

~$2,499 MSRP

NVIDIARTX 5000 Ada 32GBMejora NVIDIA
32 GB VRAM (+8)576 GB/s (+230)
C
Sube la velocidad estimada de decodificación alrededor de un 91%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 91%.

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

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run Starling LM 7B?

Yes, Tesla P40 24GB can run Starling LM 7B with a C grade (Runs well). Expected decode speed: 51.4 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 Tesla P40 24GB?

On Tesla P40 24GB, Starling LM 7B achieves approximately 51.4 tokens per second decode speed with a time-to-first-token of 3767ms using Q4_K_M quantization.

Can Tesla P40 24GB run Starling LM 7B for coding?

For coding workloads, Starling LM 7B on Tesla P40 24GB receives a C grade with 51.4 tok/s and 8K context.

What context window can Starling LM 7B use on Tesla P40 24GB?

On Tesla P40 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 Tesla P40 24GBSee all hardware for Starling LM 7B
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