Can gemma 3 12b it run on Tesla P40 24GB?

YES — Runs Great

C49Usable

Estimated from fit model

gemma 3 12b it needs ~12.3 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~28 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) — 12.3 GB, 27.9 tok/s, Runs well

12.3 GB required24.0 GB available

51% VRAM used

Fit status

Runs well

Decode

27.9 tok/s

TTFT

6942 ms

Safe context

149K

Memory

12.3 GB / 24.0 GB

Memory breakdown

Weights7.3 GB

KV Cache1.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsgemma 3 12b it 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: 27.9 tok/s decode · 6.9s TTFT (warm) · 70 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	C	Runs well	27.9 tok/s	3787 ms	149K
Coding	C	Runs well	27.9 tok/s	6942 ms	149K
Agentic Coding	C	Runs well	27.9 tok/s	10098 ms	149K
Reasoning	C	Runs well	27.9 tok/s	8204 ms	149K
RAG	C	Runs well	27.9 tok/s	12622 ms	149K

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	C45
Q3_K_S	3	5.9 GB	Low	C46
NVFP4	4	6.7 GB	Medium	C47
Q4_K_M	4	7.3 GB	Medium	C47
Q5_K_M	5	8.6 GB	High	C48
Q6_K	6	9.8 GB	High	C49
Q8_0Best for your GPU	8	12.8 GB	Very High	C50
F16	16	24.6 GB	Maximum	F0

Get started

Copy-paste commands to run gemma 3 12b it on your machine.

Run

lms load hf-maziyarpanahi--gemma-3-12b-it-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien gemma 3 12b it

RTX 5090 32GBOpción económica

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

Sube la velocidad estimada de decodificación alrededor de un 488%.164 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 488%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMejor relación calidad-precio

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

Sube la velocidad estimada de decodificación alrededor de un 268%.102.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 268%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBMejora NVIDIA

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

Sube la velocidad estimada de decodificación alrededor de un 126%.63 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 3 12b it?

Yes, Tesla P40 24GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 27.9 tok/s.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 12.3 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 12b it?

The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 12b it run at on Tesla P40 24GB?

On Tesla P40 24GB, gemma 3 12b it achieves approximately 27.9 tokens per second decode speed with a time-to-first-token of 6942ms using Q4_K_M quantization.

Can Tesla P40 24GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on Tesla P40 24GB receives a C grade with 27.9 tok/s and 149K context.

What context window can gemma 3 12b it use on Tesla P40 24GB?

On Tesla P40 24GB, gemma 3 12b it can safely use up to 149K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for gemma 3 12b it

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