Will It Run AI

Can vntl llama3 8b v2 run on NVIDIA L4 24GB?

YES — Runs Great

C48Usable
Estimated from fit model

vntl llama3 8b v2 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
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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 feelsvntl llama3 8b v2 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well40.0 tok/s2643 ms265K
CodingCRuns well40.0 tok/s4845 ms265K
Agentic CodingCRuns well40.0 tok/s7047 ms265K
ReasoningCRuns well40.0 tok/s5726 ms265K
RAGCRuns well40.0 tok/s8809 ms265K

Quantization options

How vntl llama3 8b v2 (8B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC45
Q3_K_S
3
3.9 GB
LowC45
NVFP4
4
4.5 GB
MediumC45
Q4_K_M
4
4.9 GB
MediumC46
Q5_K_M
5
5.8 GB
HighC46
Q6_K
6
6.6 GB
HighC47
Q8_0
8
8.6 GB
Very HighC48
F16Best for your GPU
16
16.4 GB
MaximumC50

Get started

Copy-paste commands to run vntl llama3 8b v2 on your machine.

Run

lms load hf-lmg-anon--vntl-llama3-8b-v2-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien vntl llama3 8b v2

NVIDIARTX 5090 32GBOpción económica
32 GB VRAM (+8)1792 GB/s (+1492)
C
Sube la velocidad estimada de decodificación alrededor de un 180%.112 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 180%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBMejor relación calidad-precio
32 GB VRAM (+8)896 GB/s (+596)
C
Sube la velocidad estimada de decodificación alrededor de un 180%.112 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 180%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

NVIDIARTX 5000 Ada 32GBMejora NVIDIA
32 GB VRAM (+8)576 GB/s (+276)
C
Sube la velocidad estimada de decodificación alrededor de un 136%.94.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 136%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run vntl llama3 8b v2?

Yes, NVIDIA L4 24GB can run vntl llama3 8b v2 with a C grade (Runs well). Expected decode speed: 40.0 tok/s.

How much VRAM does vntl llama3 8b v2 need?

vntl llama3 8b v2 (8B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.

What is the best quantization for vntl llama3 8b v2?

The recommended quantization for vntl llama3 8b v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will vntl llama3 8b v2 run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, vntl llama3 8b v2 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 vntl llama3 8b v2 for coding?

For coding workloads, vntl llama3 8b v2 on NVIDIA L4 24GB receives a C grade with 40.0 tok/s and 265K context.

What context window can vntl llama3 8b v2 use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, vntl llama3 8b v2 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 24GBSee all hardware for vntl llama3 8b v2
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