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URL: https://willitrunai.com/can-run/hf-thedrummer--gemmasutra-mini-2b-v1-gguf-on-rtx-5070-ti-16gb

⇱ Gemmasutra Mini 2B v1 on RTX 5070 Ti 16GB? YES

Can Gemmasutra Mini 2B v1 run on RTX 5070 Ti 16GB?

YES — Runs Great

C45Usable
○Estimated from fit model

Gemmasutra Mini 2B v1 needs ~4.0 GB VRAM. RTX 5070 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

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

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 5070 Ti 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well38.0 tok/s2779 ms838K
CodingCRuns well38.0 tok/s5095 ms838K
Agentic CodingCRuns well38.0 tok/s7411 ms838K
ReasoningCRuns well38.0 tok/s6021 ms838K
RAGCRuns well38.0 tok/s9263 ms838K

Quantization options

How Gemmasutra Mini 2B v1 (2B params) fits at each quantization level on RTX 5070 Ti 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.8 GB
LowC46
Q3_K_S
3
1.0 GB
LowC46
NVFP4
4
1.1 GB
MediumC46
Q4_K_M
4
1.2 GB
MediumC46
Q5_K_M
5
1.4 GB
HighC46
Q6_K
6
1.6 GB
HighC46
Q8_0
8
2.1 GB
Very HighC46
F16Best for your GPU
16
4.1 GB
MaximumC48

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

See all results for RTX 5070 Ti 16GBSee all hardware for Gemmasutra Mini 2B v1