Will It Run AI

Can internlm2 math plus 20b i1 run on NVIDIA L4 24GB?

YES — Runs Great

C51Usable
Estimated from fit model

internlm2 math plus 20b i1 needs ~18.1 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~16 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) — 18.1 GB, 16.0 tok/s, Runs well
18.1 GB required24.0 GB available
75% VRAM used

Fit status

Runs well

Decode

16.0 tok/s

TTFT

12112 ms

Safe context

56K

Memory

18.1 GB / 24.0 GB

Memory breakdown

Weights12.2 GB
KV Cache2.3 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 20b i1 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: 16.0 tok/s decode · 12.1s TTFT (warm) · 40 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 well16.0 tok/s6607 ms56K
CodingCRuns well16.0 tok/s12112 ms56K
Agentic CodingCTight fit16.0 tok/s17618 ms56K
ReasoningCRuns well16.0 tok/s14315 ms56K
RAGCTight fit16.0 tok/s22023 ms56K

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowC47
Q3_K_S
3
9.8 GB
LowC48
NVFP4
4
11.2 GB
MediumC49
Q4_K_M
4
12.2 GB
MediumC50
Q5_K_M
5
14.4 GB
HighC49
Q6_KBest for your GPU
6
16.4 GB
HighC49
Q8_0
8
21.4 GB
Very HighF0
F16
16
41.0 GB
MaximumF0

Get started

Copy-paste commands to run internlm2 math plus 20b i1 on your machine.

Run

lms load hf-mradermacher--internlm2-math-plus-20b-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien internlm2 math plus 20b i1

NVIDIARTX 5090 32GBOpción económica
32 GB VRAM (+8)1792 GB/s (+1492)
C
Sube la velocidad estimada de decodificación alrededor de un 515%.98.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 515%.

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 286%.61.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 286%.

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%.37.8 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 internlm2 math plus 20b i1?

Yes, NVIDIA L4 24GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 16.0 tok/s.

How much VRAM does internlm2 math plus 20b i1 need?

internlm2 math plus 20b i1 (20B parameters) requires approximately 18.1 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm2 math plus 20b i1?

The recommended quantization for internlm2 math plus 20b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm2 math plus 20b i1 run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, internlm2 math plus 20b i1 achieves approximately 16.0 tokens per second decode speed with a time-to-first-token of 12112ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on NVIDIA L4 24GB receives a C grade with 16.0 tok/s and 56K context.

What context window can internlm2 math plus 20b i1 use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, internlm2 math plus 20b i1 can safely use up to 56K 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 internlm2 math plus 20b i1
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