Can Gemmasutra Mini 2B v1 run on RTX 5080 16GB?

YES — Runs Great

C45Usable

Estimated from fit model

Gemmasutra Mini 2B v1 needs ~4.0 GB VRAM. RTX 5080 16GB has 16.0 GB. With Q4_K_M quantization, expect ~38 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) — 4.0 GB, 38.0 tok/s, Runs well

4.0 GB required16.0 GB available

25% VRAM used

Fit status

Runs well

Decode

38.0 tok/s

TTFT

5095 ms

Safe context

838K

Memory

4.0 GB / 16.0 GB

Memory breakdown

Weights1.2 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemmasutra Mini 2B v1 on RTX 5080 16GB
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: 38.0 tok/s decode · 5.1s TTFT (warm) · 95 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	38.0 tok/s	2779 ms	838K
Coding	C	Runs well	38.0 tok/s	5095 ms	838K
Agentic Coding	C	Runs well	38.0 tok/s	7411 ms	838K
Reasoning	C	Runs well	38.0 tok/s	6021 ms	838K
RAG	C	Runs well	38.0 tok/s	9263 ms	838K

Quantization options

How Gemmasutra Mini 2B v1 (2B params) fits at each quantization level on RTX 5080 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.8 GB	Low	C46
Q3_K_S	3	1.0 GB	Low	C46
NVFP4	4	1.1 GB	Medium	C46
Q4_K_M	4	1.2 GB	Medium	C46
Q5_K_M	5	1.4 GB	High	C46
Q6_K	6	1.6 GB	High	C46
Q8_0	8	2.1 GB	Very High	C46
F16Best for your GPU	16	4.1 GB	Maximum	C48

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

Frequently asked questions

Can RTX 5080 16GB run Gemmasutra Mini 2B v1?

Yes, RTX 5080 16GB can run Gemmasutra Mini 2B v1 with a C grade (Runs well). Expected decode speed: 38.0 tok/s.

How much VRAM does Gemmasutra Mini 2B v1 need?

Gemmasutra Mini 2B v1 (2B parameters) requires approximately 4.0 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 RTX 5080 16GB?

On RTX 5080 16GB, Gemmasutra Mini 2B v1 achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5095ms using Q4_K_M quantization.

Can RTX 5080 16GB run Gemmasutra Mini 2B v1 for coding?

For coding workloads, Gemmasutra Mini 2B v1 on RTX 5080 16GB receives a C grade with 38.0 tok/s and 838K context.

What context window can Gemmasutra Mini 2B v1 use on RTX 5080 16GB?

On RTX 5080 16GB, Gemmasutra Mini 2B v1 can safely use up to 838K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5080 16GB See all hardware for Gemmasutra Mini 2B v1

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