Can Samantha 7B run on NVIDIA L4 24GB?

YES — Runs Great

B65Good

Estimated from fit model

Samantha 7B needs ~9.8 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 9.8 GB, 49.1 tok/s, Runs well

9.8 GB required24.0 GB available

41% VRAM used

Fit status

Runs well

Decode

49.1 tok/s

TTFT

3944 ms

Safe context

Memory

9.8 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsSamantha 7B on NVIDIA L4 24GB
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: 49.1 tok/s decode · 3.9s TTFT (warm) · 123 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	B	Runs well	49.1 tok/s	2151 ms	4K
Coding	B	Runs well	49.1 tok/s	3944 ms	4K
Agentic Coding	B	Runs well	49.1 tok/s	5736 ms	4K
Reasoning	B	Runs well	49.1 tok/s	4661 ms	4K
RAG	B	Runs well	49.1 tok/s	7170 ms	4K

Quantization options

How Samantha 7B (7B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B60
Q3_K_S	3	3.4 GB	Low	B61
NVFP4	4

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

Upgrade options

Hardware that runs Samantha 7B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 100%.98 tok/s decode

Raises estimated decode speed by about 100%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 100%.98 tok/s decode

Raises estimated decode speed by about 100%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)576 GB/s (+276)

Raises estimated decode speed by about 100%.98 tok/s decode

Raises estimated decode speed by about 100%.

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

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Samantha 7B?

Yes, NVIDIA L4 24GB can run Samantha 7B with a B grade (Runs well). Expected decode speed: 49.1 tok/s.

How much VRAM does Samantha 7B need?

Samantha 7B (7B parameters) requires approximately 9.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Samantha 7B?

The recommended quantization for Samantha 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Samantha 7B run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Samantha 7B achieves approximately 49.1 tokens per second decode speed with a time-to-first-token of 3944ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Samantha 7B for coding?

For coding workloads, Samantha 7B on NVIDIA L4 24GB receives a B grade with 49.1 tok/s and 4K context.

What context window can Samantha 7B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Samantha 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for Samantha 7B

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