Can Dolphin3.0 Llama3.1 8B run on NVIDIA L4 24GB?

YES — Runs Great

C48Usable

Estimated from fit model

Dolphin3.0 Llama3.1 8B needs ~9.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~40 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) — 9.4 GB, 40.0 tok/s, Runs well

9.4 GB required24.0 GB available

39% VRAM used

Fit status

Runs well

Decode

40.0 tok/s

TTFT

4845 ms

Safe context

265K

Memory

9.4 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsDolphin3.0 Llama3.1 8B 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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 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	40.0 tok/s	2643 ms	265K
Coding	C	Runs well	40.0 tok/s	4845 ms	265K
Agentic Coding	C	Runs well	40.0 tok/s	7047 ms	265K
Reasoning	C	Runs well	40.0 tok/s	5726 ms	265K
RAG	C	Runs well	40.0 tok/s	8809 ms	265K

Quantization options

How Dolphin3.0 Llama3.1 8B (8B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C45
NVFP4	4	4.5 GB	Medium	C45
Q4_K_M	4	4.9 GB	Medium	C46
Q5_K_M	5	5.8 GB	High	C46
Q6_K	6	6.6 GB	High	C47
Q8_0	8	8.6 GB	Very High	C48
F16Best for your GPU	16	16.4 GB	Maximum	C50

Get started

Copy-paste commands to run Dolphin3.0 Llama3.1 8B on your machine.

Run

lms load hf-dphn--dolphin3-0-llama3-1-8b-gguf && lms server start

アップグレードオプション

Dolphin3.0 Llama3.1 8Bを快適に動かすハードウェア

RTX 5090 32GBコスパ重視

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

Raises estimated decode speed by about 180%.112 tok/sデコード

Raises estimated decode speed by about 180%.

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

〜$1,999 MSRP

RTX PRO 4500 Blackwell 32GBコスパ最良

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

Raises estimated decode speed by about 180%.112 tok/sデコード

Raises estimated decode speed by about 180%.

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

〜$2,499 MSRP

RTX 5000 Ada 32GBNVIDIAアップグレード

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

Raises estimated decode speed by about 136%.94.4 tok/sデコード

Raises estimated decode speed by about 136%.

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

〜$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Dolphin3.0 Llama3.1 8B?

Yes, NVIDIA L4 24GB can run Dolphin3.0 Llama3.1 8B with a C grade (Runs well). Expected decode speed: 40.0 tok/s.

How much VRAM does Dolphin3.0 Llama3.1 8B need?

Dolphin3.0 Llama3.1 8B (8B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Dolphin3.0 Llama3.1 8B?

The recommended quantization for Dolphin3.0 Llama3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Dolphin3.0 Llama3.1 8B run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Dolphin3.0 Llama3.1 8B achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4845ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Dolphin3.0 Llama3.1 8B for coding?

For coding workloads, Dolphin3.0 Llama3.1 8B on NVIDIA L4 24GB receives a C grade with 40.0 tok/s and 265K context.

What context window can Dolphin3.0 Llama3.1 8B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Dolphin3.0 Llama3.1 8B can safely use up to 265K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for Dolphin3.0 Llama3.1 8B

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