Can Samantha 7B run on RTX 3080 10GB?

YES — Runs Great

A73Great

○Estimated from fit model

Samantha 7B needs ~8.1 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~84 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 8.1 GB, 84.0 tok/s, Runs well

8.1 GB required10.0 GB available

81% VRAM used

Fit status

Runs well

Decode

84.0 tok/s

TTFT

2305 ms

Safe context

Memory

8.1 GB / 10.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsSamantha 7B on RTX 3080 10GB
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: 84.0 tok/s decode · 2.3s TTFT (warm) · 210 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	A	Runs well	84.0 tok/s	1257 ms	4K
Coding	A	Runs well	84.0 tok/s	2305 ms	4K
Agentic Coding	B	Runs with offload (needs ~0 GB host RAM)	84.0 tok/s	3352 ms	4K
Reasoning	A	Runs well	84.0 tok/s	2724 ms	4K
RAG	B	Runs with offload (needs ~0 GB host RAM)	84.0 tok/s	4190 ms	4K

Quantization options

How Samantha 7B (7B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B67
Q3_K_S	3	3.4 GB	Low	B68
NVFP4	4	3.9 GB	Medium	B69
Q4_K_M	4	4.3 GB	Medium	B69
Q5_K_M	5	5.0 GB	High	B69
Q6_KBest for your GPU	6	5.7 GB	High	B69
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run Samantha 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "cognitivecomputations/samantha-1.1-llama-7b" \
 --hf-file "samantha-1.1-llama-7b-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your RTX 3080 10GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	91.2 tok/s
👁 Alibaba Qwen 3 8B	8B	S	96 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	S	96 tok/s
👁 InternLM InternVL2 8B	8B	S	96 tok/s
👁 Mistral Ministral 3 8B	8B	A	96 tok/s

Frequently asked questions

See all results for RTX 3080 10GB See all hardware for Samantha 7B

URL: https://willitrunai.com/can-run/samantha-7b-on-rtx-3080-10gb