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

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

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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	39.0 tok/s	2705 ms	8K
Coding	B	Runs well	39.0 tok/s	4959 ms	8K
Agentic Coding	B	Runs well	39.0 tok/s	7213 ms	8K
Reasoning	B	Runs well	39.0 tok/s	5860 ms	8K
RAG	B	Runs well	39.0 tok/s	9016 ms	8K

Quantization options

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

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B59
Q3_K_S	3	4.4 GB	Low	B60
NVFP4	4

Get started

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

Run

ollama run gemma2

Upgrade options

Hardware that runs Gemma 2 9B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1446)

Raises estimated decode speed by about 223%.126 tok/s decode

Raises estimated decode speed by about 223%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+550)

Raises estimated decode speed by about 223%.126 tok/s decode

Raises estimated decode speed by about 223%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)576 GB/s (+230)

Raises estimated decode speed by about 126%.88.1 tok/s decode

Raises estimated decode speed by about 126%.

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

~$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 24GB See all hardware for Gemma 2 9B

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