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URL: https://willitrunai.com/can-run/hf-intervitens-archive--internlm2-limarp-chat-20b-gguf-on-rtx-5090-laptop-24gb

⇱ internlm2 limarp chat 20b on RTX 5090 Laptop 24GB? YES

Can internlm2 limarp chat 20b run on RTX 5090 Laptop 24GB?

YES — Runs Great

B55Good
○Estimated from fit model

internlm2 limarp chat 20b needs ~18.1 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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, 61.7 tok/s, Runs well
18.1 GB required24.0 GB available
75% VRAM used

Fit status

Runs well

Decode

61.7 tok/s

TTFT

3138 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 limarp chat 20b on RTX 5090 Laptop 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: 61.7 tok/s decode · 3.1s TTFT (warm) · 154 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
ChatBRuns well61.7 tok/s1712 ms56K
CodingBRuns well61.7 tok/s3138 ms56K
Agentic CodingCTight fit61.7 tok/s4565 ms56K
ReasoningBRuns well61.7 tok/s3709 ms56K
RAGCTight fit61.7 tok/s5706 ms56K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on RTX 5090 Laptop 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
HighC50
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 limarp chat 20b on your machine.

Run

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

Frequently asked questions

See all results for RTX 5090 Laptop 24GBSee all hardware for internlm2 limarp chat 20b