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URL: https://willitrunai.com/can-run/hf-intervitens-archive--internlm2-limarp-chat-20b-gguf-on-a4000-16gb

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Can internlm2 limarp chat 20b run on RTX A4000 16GB?

YES — With Offload

D38Poor
○Estimated from fit model

internlm2 limarp chat 20b needs ~17.0 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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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) — 17.0 GB, 16.9 tok/s, Runs with offload (needs ~0.7 GB host RAM)
17.0 GB required16.0 GB available
106% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.7 GB host RAM)

Decode

16.9 tok/s

TTFT

11471 ms

Safe context

9K

Memory

17.0 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights12.2 GB
KV Cache2.3 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm2 limarp chat 20b on RTX A4000 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: 16.9 tok/s decode · 11.5s TTFT (warm) · 42 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload25.7 tok/s4108 ms9K
CodingDRuns with offload16.9 tok/s11471 ms9K
Agentic CodingFToo heavy12.9 tok/s21883 ms9K
ReasoningDRuns with offload (needs ~0.7 GB host RAM)16.9 tok/s13557 ms9K
RAGFToo heavy12.9 tok/s27354 ms9K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowC51
Q3_K_S
3
9.8 GB
LowC51
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

👁 NVIDIA
RTX 4000 Ada 20GBBudget pick
20 GB VRAM (+4)
C
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.

Raises estimated decode speed by about 36%.

~$1,250 MSRP

👁 NVIDIA
RTX 3090 24GBBest value
24 GB VRAM (+8)936 GB/s (+488)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.46.7 tok/s decode

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

Raises estimated decode speed by about 176%.

~$1,499 MSRP

👁 NVIDIA
RTX 4090 24GBNVIDIA upgrade
24 GB VRAM (+8)1008 GB/s (+560)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.50.9 tok/s decode

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

Raises estimated decode speed by about 201%.

~$1,599 MSRP

Frequently asked questions

See all results for RTX A4000 16GBSee all hardware for internlm2 limarp chat 20b
11.2 GB
Medium
C50
Q4_K_MBest for your GPU
4
12.2 GB
MediumC50
Q5_K_M
5
14.4 GB
HighF0
Q6_K
6
16.4 GB
HighF0
Q8_0
8
21.4 GB
Very HighF0
F16
16
41.0 GB
MaximumF0

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.