willitrun·ai

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
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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) — 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well32.0 tok/s3300 ms2.8M
CodingCRuns well32.0 tok/s6050 ms2.8M
Agentic CodingCRuns well32.0 tok/s8800 ms2.8M
ReasoningCRuns well32.0 tok/s7150 ms2.8M
RAGCRuns well32.0 tok/s11000 ms2.8M

Inference speed

Gemmasutra Mini 2B v1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemmasutra Mini 2B v1 at Q4_K_M 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M38.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M32.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M32.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M28.0Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M28.0Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M28.0Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M28.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M28.0Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M28.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M28.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M28.0Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M28.0Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M28.0Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M28.0Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M28.0Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M28.0Fits

Estimates for single-stream decoding at Q4_K_M; 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 Gemmasutra Mini 2B v1 (2B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.8 GB
LowC41
Q3_K_S
3
1.0 GB
LowC41
NVFP4
4
1.1 GB
MediumC41
Q4_K_M
4
1.2 GB
MediumC41
Q5_K_M
5
1.4 GB
HighC41
Q6_K
6
1.6 GB
HighC41
Q8_0
8
2.1 GB
Very HighC42
F16Best for your GPU
16
4.1 GB
MaximumC42

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

升级选项

能流畅运行 Gemmasutra Mini 2B v1 的硬件

MacBook Pro M4 Max 96GB经济之选
96 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.28 tok/s 解码

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

~$2,499 MSRP

MacBook Pro M3 Max 128GB最佳性价比
128 GB Unified (+80)
C
Adds memory headroom for longer context windows and future model growth.28 tok/s 解码

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

~$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 48GBSee all hardware for Gemmasutra Mini 2B v1
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