Can Gemmasutra Mini 2B v1 run on NVIDIA L20 48GB?

YES — Runs Great

C42Usable

Estimated from fit model

Gemmasutra Mini 2B v1 needs ~7.2 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M 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

Q4_K_M (Medium quality) — 7.2 GB, 32.0 tok/s, Runs well

7.2 GB required48.0 GB available

15% VRAM used

Fit status

Runs well

Decode

32.0 tok/s

TTFT

6050 ms

Safe context

2.8M

Memory

7.2 GB / 48.0 GB

Memory breakdown

Weights1.2 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsGemmasutra Mini 2B v1 on NVIDIA L20 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

Quantization options

How Gemmasutra Mini 2B v1 (2B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.8 GB	Low	C41
Q3_K_S	3	1.0 GB	Low	C41
NVFP4	4	1.1 GB	Medium	C41
Q4_K_M	4	1.2 GB	Medium	C41
Q5_K_M	5	1.4 GB	High	C41
Q6_K	6	1.6 GB	High	C41
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 Gemmasutra Mini 2B v1 on your machine.

Run

lms load hf-thedrummer--gemmasutra-mini-2b-v1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Gemmasutra Mini 2B v1

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 L20 48GB run Gemmasutra Mini 2B v1?

Yes, NVIDIA L20 48GB can run Gemmasutra Mini 2B v1 with a C grade (Runs well). Expected decode speed: 32.0 tok/s.

How much VRAM does Gemmasutra Mini 2B v1 need?

Gemmasutra Mini 2B v1 (2B parameters) requires approximately 7.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemmasutra Mini 2B v1?

The recommended quantization for Gemmasutra Mini 2B v1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemmasutra Mini 2B v1 run at on NVIDIA L20 48GB?

On NVIDIA L20 48GB, Gemmasutra Mini 2B v1 achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6050ms using Q4_K_M quantization.

Can NVIDIA L20 48GB run Gemmasutra Mini 2B v1 for coding?

For coding workloads, Gemmasutra Mini 2B v1 on NVIDIA L20 48GB receives a C grade with 32.0 tok/s and 2.8M context.

What context window can Gemmasutra Mini 2B v1 use on NVIDIA L20 48GB?

On NVIDIA L20 48GB, Gemmasutra Mini 2B v1 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 L20 48GB See all hardware for Gemmasutra Mini 2B v1

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