Will It Run AI

Can Gemma 2 9B run on Tesla P40 24GB?

YES — Runs Great

B66Good
Estimated from fit model

Gemma 2 9B needs ~14.2 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~39 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) — 14.2 GB, 39.0 tok/s, Runs well
14.2 GB required24.0 GB available
59% VRAM used

Fit status

Runs well

Decode

39.0 tok/s

TTFT

4959 ms

Safe context

8K

Memory

14.2 GB / 24.0 GB

Memory breakdown

Weights5.5 GB
KV Cache5.1 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 2 9B 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: 39.0 tok/s decode · 5.0s TTFT (warm) · 98 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
ChatBRuns well39.0 tok/s2705 ms8K
CodingBRuns well39.0 tok/s4959 ms8K
Agentic CodingBRuns well39.0 tok/s7213 ms8K
ReasoningBRuns well39.0 tok/s5860 ms8K
RAGBRuns well39.0 tok/s9016 ms8K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowB59
Q3_K_S
3
4.4 GB
LowB60
NVFP4
4
5.0 GB
MediumB60
Q4_K_M
4
5.5 GB
MediumB60
Q5_K_M
5
6.5 GB
HighB61
Q6_K
6
7.4 GB
HighB61
Q8_0
8
9.6 GB
Very HighB63
F16Best for your GPU
16
18.5 GB
MaximumB64

Get started

Copy-paste commands to run Gemma 2 9B on your machine.

Run

ollama run gemma2

Opciones de mejora

Hardware que ejecuta bien Gemma 2 9B

NVIDIARTX 5090 32GBOpción económica
32 GB VRAM (+8)1792 GB/s (+1446)
B
Sube la velocidad estimada de decodificación alrededor de un 223%.126 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 223%.

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)
B
Sube la velocidad estimada de decodificación alrededor de un 223%.126 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 223%.

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)
B
Sube la velocidad estimada de decodificación alrededor de un 126%.88.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 126%.

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 Gemma 2 9B?

Yes, Tesla P40 24GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 39.0 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 14.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 9B?

The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 9B run at on Tesla P40 24GB?

On Tesla P40 24GB, Gemma 2 9B achieves approximately 39.0 tokens per second decode speed with a time-to-first-token of 4959ms using Q4_K_M quantization.

Can Tesla P40 24GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on Tesla P40 24GB receives a B grade with 39.0 tok/s and 8K context.

What context window can Gemma 2 9B use on Tesla P40 24GB?

On Tesla P40 24GB, Gemma 2 9B 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 Gemma 2 9B
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