Can internlm2 limarp chat 20b run on RTX 5000 Ada Laptop 16GB?

Q: What should I upgrade first if internlm2 limarp chat 20b feels slow on RTX 5000 Ada Laptop 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

D39Poor

Estimated from fit model

internlm2 limarp chat 20b needs ~17.3 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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) — 17.3 GB, 21.8 tok/s, Very compromised (needs ~0.9 GB host RAM)

17.3 GB required16.0 GB available

108% VRAM needed

1.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.9 GB host RAM)

Decode

21.8 tok/s

TTFT

8875 ms

Safe context

Memory

17.3 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm2 limarp chat 20b on RTX 5000 Ada Laptop 16GB
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: 21.8 tok/s decode · 8.9s TTFT (warm) · 55 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.1 GB host RAM)	25.3 tok/s	4178 ms	7K
Coding	D	Very compromised	21.8 tok/s	8875 ms	7K
Agentic Coding	F	Too heavy	16.7 tok/s	16857 ms	7K
Reasoning	D	Very compromised (needs ~0.9 GB host RAM)	21.8 tok/s	10489 ms	7K
RAG	F	Too heavy	16.7 tok/s	21071 ms

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C51
Q3_K_S	3	9.8 GB	Low	C51
NVFP4	4

Get started

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

Run

lms load hf-intervitens-archive--internlm2-limarp-chat-20b-gguf && lms server start

Upgrade options

Hardware that runs internlm2 limarp chat 20b well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.23 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+360)

Removes host-memory offload, which is usually the single biggest latency and throughput win.53.7 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 146%.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+432)

Removes host-memory offload, which is usually the single biggest latency and throughput win.62.8 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 188%.

~$1,599 MSRP

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run internlm2 limarp chat 20b?

Yes, RTX 5000 Ada Laptop 16GB can run internlm2 limarp chat 20b with a D grade (Very compromised). Expected decode speed: 21.8 tok/s.

How much VRAM does internlm2 limarp chat 20b need?

internlm2 limarp chat 20b (20B parameters) requires approximately 17.3 GB of memory with Q4_K_M quantization.

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

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

What speed will internlm2 limarp chat 20b run at on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, internlm2 limarp chat 20b achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8875ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run internlm2 limarp chat 20b for coding?

For coding workloads, internlm2 limarp chat 20b on RTX 5000 Ada Laptop 16GB receives a D grade with 21.8 tok/s and 7K context.

What context window can internlm2 limarp chat 20b use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, internlm2 limarp chat 20b can safely use up to 7K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada Laptop 16GB See all hardware for internlm2 limarp chat 20b

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