Can internlm2 limarp chat 20b run on RTX PRO 5000 Blackwell 48GB?

YES — Runs Great

C51Usable

Estimated from fit model

internlm2 limarp chat 20b needs ~20.5 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~93 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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, 92.5 tok/s, Runs well

20.5 GB required48.0 GB available

43% VRAM used

Fit status

Runs well

Decode

92.5 tok/s

TTFT

2092 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 PRO 5000 Blackwell 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: 92.5 tok/s decode · 2.1s TTFT (warm) · 231 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	92.5 tok/s	1141 ms	203K
Coding	C	Runs well	92.5 tok/s	2092 ms	203K
Agentic Coding	C	Runs well	92.5 tok/s	3043 ms	203K
Reasoning	C	Runs well	92.5 tok/s	2473 ms	203K
RAG	C	Runs well	92.5 tok/s	3804 ms	203K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on RTX PRO 5000 Blackwell 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

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

Can RTX PRO 5000 Blackwell 48GB run internlm2 limarp chat 20b?

Yes, RTX PRO 5000 Blackwell 48GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 92.5 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 PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, internlm2 limarp chat 20b achieves approximately 92.5 tokens per second decode speed with a time-to-first-token of 2092ms using Q4_K_M quantization.

Can RTX PRO 5000 Blackwell 48GB run internlm2 limarp chat 20b for coding?

For coding workloads, internlm2 limarp chat 20b on RTX PRO 5000 Blackwell 48GB receives a C grade with 92.5 tok/s and 203K context.

What context window can internlm2 limarp chat 20b use on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 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 PRO 5000 Blackwell 48GB See all hardware for internlm2 limarp chat 20b

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