Can gemma 2 2b it run on NVIDIA L40 48GB?

YES — Runs Great

C42Usable

Estimated from fit model

gemma 2 2b it needs ~7.6 GB VRAM. NVIDIA L40 48GB has 48.0 GB. With Q6_K quantization, expect ~32 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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

Q6_K (High quality) — 7.6 GB, 32.0 tok/s, Runs well

7.6 GB required48.0 GB available

16% VRAM used

Fit status

Runs well

Decode

32.0 tok/s

TTFT

6050 ms

Safe context

2.8M

Memory

7.6 GB / 48.0 GB

Memory breakdown

Weights1.6 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsgemma 2 2b it on NVIDIA L40 48GB
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: 32.0 tok/s decode · 6.0s TTFT (warm) · 80 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	32.0 tok/s	3300 ms	2.8M
Coding	C	Runs well	32.0 tok/s	6050 ms	2.8M
Agentic Coding	C	Runs well	32.0 tok/s	8800 ms	2.8M
Reasoning	C	Runs well	32.0 tok/s	7150 ms	2.8M
RAG	C	Runs well	32.0 tok/s	11000 ms	2.8M

Inference speed

gemma 2 2b it inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for gemma 2 2b it at Q6_K across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~38 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q6_K	38.0	Fits
RTX 4090 24GB	24 GB	Q6_K	32.0	Fits
RTX 4080 Super 16GB	16 GB	Q6_K	32.0	Fits
RTX 3090 24GB	24 GB	Q6_K	28.0	Fits
RTX 4070 12GB	12 GB	Q6_K	28.0	Fits
RTX 3060 12GB	12 GB	Q6_K	28.0	Fits
RTX 4060 8GB	8 GB	Q6_K	28.0	Fits
RX 7900 XTX 24GB	24 GB	Q6_K	28.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q6_K	28.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q6_K	28.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q6_K	28.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q6_K	28.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q6_K	28.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q6_K	28.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q6_K	28.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q6_K	28.0	Fits

Estimates for single-stream decoding at Q6_K; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How gemma 2 2b it (2B params) fits at each quantization level on NVIDIA L40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.8 GB	Low	C42
Q3_K_S	3	1.0 GB	Low	C42
NVFP4	4	1.1 GB	Medium	C42
Q4_K_M	4	1.2 GB	Medium	C42
Q5_K_M	5	1.4 GB	High	C42
Q6_K	6	1.6 GB	High	C42
Q8_0	8	2.1 GB	Very High	C42
F16Best for your GPU	16	4.1 GB	Maximum	C42

Get started

Copy-paste commands to run gemma 2 2b it on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bartowski/gemma-2-2b-it-GGUF" \
  --hf-file "gemma-2-2b-it-GGUF-Q6_K.gguf" \
  -c 4096 -ngl 99

Opciones de mejora

Hardware que ejecuta bien gemma 2 2b it

MacBook Pro M4 Max 96GBOpción económica

96 GB Unified (+48)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.28 tok/s decodificación

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

~$2,499 MSRP

MacBook Pro M3 Max 128GBMejor relación calidad-precio

128 GB Unified (+80)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.28 tok/s decodificación

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

~$2,499 MSRP

Frequently asked questions

Can NVIDIA L40 48GB run gemma 2 2b it?

Yes, NVIDIA L40 48GB can run gemma 2 2b it with a C grade (Runs well). Expected decode speed: 32.0 tok/s.

How much VRAM does gemma 2 2b it need?

gemma 2 2b it (2B parameters) requires approximately 7.6 GB of memory with Q6_K quantization.

What is the best quantization for gemma 2 2b it?

The recommended quantization for gemma 2 2b it is Q6_K, which balances quality and memory efficiency.

What speed will gemma 2 2b it run at on NVIDIA L40 48GB?

On NVIDIA L40 48GB, gemma 2 2b it achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6050ms using Q6_K quantization.

Can NVIDIA L40 48GB run gemma 2 2b it for coding?

For coding workloads, gemma 2 2b it on NVIDIA L40 48GB receives a C grade with 32.0 tok/s and 2.8M context.

What context window can gemma 2 2b it use on NVIDIA L40 48GB?

On NVIDIA L40 48GB, gemma 2 2b it can safely use up to 2.8M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L40 48GB See all hardware for gemma 2 2b it

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