Can internlm2 limarp chat 20b run on Quadro RTX 8000 48GB?

YES — Runs Great

C48Usable

Estimated from fit model

internlm2 limarp chat 20b needs ~20.5 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 20.5 GB, 38.0 tok/s, Runs well

20.5 GB required48.0 GB available

43% VRAM used

Fit status

Runs well

Decode

38.0 tok/s

TTFT

5094 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 Quadro RTX 8000 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: 38.0 tok/s decode · 5.1s TTFT (warm) · 95 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	38.0 tok/s	2778 ms	203K
Coding	C	Runs well	38.0 tok/s	5094 ms	203K
Agentic Coding	C	Runs well	38.0 tok/s	7409 ms	203K
Reasoning	C	Runs well	38.0 tok/s	6020 ms	203K
RAG	C	Runs well	38.0 tok/s	9262 ms	203K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C42
Q3_K_S	3	9.8 GB	Low	C42
NVFP4	4	11.2 GB	Medium	C43
Q4_K_M	4	12.2 GB	Medium	C43
Q5_K_M	5	14.4 GB	High	C44
Q6_K	6	16.4 GB	High	C44
Q8_0	8	21.4 GB	Very High	C46
F16Best for your GPU	16	41.0 GB	Maximum	C47

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

Opções de upgrade

Hardware que roda bem internlm2 limarp chat 20b

NVIDIA A100 80GBOpção econômica

80 GB VRAM (+32)2039 GB/s (+1367)

Raises estimated decode speed by about 269%.140.4 tok/s decodificação

Raises estimated decode speed by about 269%.

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

~$15,000 MSRP

NVIDIA A800 80GBMelhor custo-benefício

80 GB VRAM (+32)1935 GB/s (+1263)

Raises estimated decode speed by about 226%.123.7 tok/s decodificação

Raises estimated decode speed by about 226%.

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

~$15,000 MSRP

NVIDIA H800 80GBUpgrade NVIDIA

80 GB VRAM (+32)3000 GB/s (+2328)

Raises estimated decode speed by about 424%.199.2 tok/s decodificação

Raises estimated decode speed by about 424%.

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

~$30,000 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run internlm2 limarp chat 20b?

Yes, Quadro RTX 8000 48GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 38.0 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, internlm2 limarp chat 20b achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5094ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run internlm2 limarp chat 20b for coding?

For coding workloads, internlm2 limarp chat 20b on Quadro RTX 8000 48GB receives a C grade with 38.0 tok/s and 203K context.

What context window can internlm2 limarp chat 20b use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 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 Quadro RTX 8000 48GB See all hardware for internlm2 limarp chat 20b

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