Can internlm2 5 20b chat run on NVIDIA L4 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

internlm2 5 20b chat 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

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 5 20b chat 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	16.0 tok/s	6607 ms	56K
Coding	C	Runs well	16.0 tok/s	12112 ms	56K
Agentic Coding	C	Tight fit	16.0 tok/s	17618 ms	56K
Reasoning	C	Runs well	16.0 tok/s	14315 ms	56K
RAG	C	Tight fit	16.0 tok/s	22023 ms	56K

Quantization options

How internlm2 5 20b chat (20B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C47
Q3_K_S	3	9.8 GB	Low	C48
NVFP4	4

Get started

Copy-paste commands to run internlm2 5 20b chat on your machine.

Run

lms load hf-bartowski--internlm2-5-20b-chat-gguf && lms server start

Upgrade options

Hardware that runs internlm2 5 20b chat well

RTX 5090 32GBBudget pick

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

Raises estimated decode speed by about 515%.98.4 tok/s decode

Raises estimated decode speed by about 515%.

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 286%.61.7 tok/s decode

Raises estimated decode speed by about 286%.

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 136%.37.8 tok/s decode

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 internlm2 5 20b chat?

Yes, NVIDIA L4 24GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 16.0 tok/s.

How much VRAM does internlm2 5 20b chat need?

internlm2 5 20b chat (20B parameters) requires approximately 18.1 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm2 5 20b chat?

The recommended quantization for internlm2 5 20b chat is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm2 5 20b chat run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, internlm2 5 20b chat 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 5 20b chat for coding?

For coding workloads, internlm2 5 20b chat on NVIDIA L4 24GB receives a C grade with 16.0 tok/s and 56K context.

What context window can internlm2 5 20b chat use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, internlm2 5 20b chat 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 24GB See all hardware for internlm2 5 20b chat

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