Will It Run AI

Can internlm2 limarp chat 20b run on RTX A6000 48GB?

YES — Runs Great

C48Usable
Estimated from fit model

internlm2 limarp chat 20b needs ~20.5 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 20.5 GB, 47.8 tok/s, Runs well
20.5 GB required48.0 GB available
43% VRAM used

Fit status

Runs well

Decode

47.8 tok/s

TTFT

4047 ms

Safe context

203K

Memory

20.5 GB / 48.0 GB

Memory breakdown

Weights12.2 GB
KV Cache2.3 GB
Runtime1.2 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsinternlm2 limarp chat 20b on RTX A6000 48GB
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: 47.8 tok/s decode · 4.0s TTFT (warm) · 120 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 well47.8 tok/s2207 ms203K
CodingCRuns well47.8 tok/s4047 ms203K
Agentic CodingCRuns well47.8 tok/s5886 ms203K
ReasoningCRuns well47.8 tok/s4782 ms203K
RAGCRuns well47.8 tok/s7357 ms203K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowC42
Q3_K_S
3
9.8 GB
LowC42
NVFP4
4
11.2 GB
MediumC43
Q4_K_M
4
12.2 GB
MediumC43
Q5_K_M
5
14.4 GB
HighC44
Q6_K
6
16.4 GB
HighC44
Q8_0
8
21.4 GB
Very HighC46
F16Best for your GPU
16
41.0 GB
MaximumC47

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

Opciones de mejora

Hardware que ejecuta bien internlm2 limarp chat 20b

AMD Instinct MI210 64GBOpción económica
64 GB VRAM (+16)1638 GB/s (+870)
C
Sube la velocidad estimada de decodificación alrededor de un 91%.91.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 91%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$10,000 MSRP

Frequently asked questions

Can RTX A6000 48GB run internlm2 limarp chat 20b?

Yes, RTX A6000 48GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 47.8 tok/s.

How much VRAM does internlm2 limarp chat 20b need?

internlm2 limarp chat 20b (20B parameters) requires approximately 20.5 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 A6000 48GB?

On RTX A6000 48GB, internlm2 limarp chat 20b achieves approximately 47.8 tokens per second decode speed with a time-to-first-token of 4047ms using Q4_K_M quantization.

Can RTX A6000 48GB run internlm2 limarp chat 20b for coding?

For coding workloads, internlm2 limarp chat 20b on RTX A6000 48GB receives a C grade with 47.8 tok/s and 203K context.

What context window can internlm2 limarp chat 20b use on RTX A6000 48GB?

On RTX A6000 48GB, internlm2 limarp chat 20b can safely use up to 203K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A6000 48GBSee all hardware for internlm2 limarp chat 20b
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