Will It Run AI

Can internlm2 limarp chat 20b run on RTX PRO 6000 Blackwell Server Edition 96GB?

YES — Runs Great

C47Usable
Estimated from fit model

internlm2 limarp chat 20b needs ~25.3 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~110 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 25.3 GB, 110.0 tok/s, Runs well
25.3 GB required96.0 GB available
26% VRAM used

Fit status

Runs well

Decode

110.0 tok/s

TTFT

1761 ms

Safe context

498K

Memory

25.3 GB / 96.0 GB

Memory breakdown

Weights12.2 GB
KV Cache2.3 GB
Runtime1.2 GB
Headroom9.6 GB

See how fast it feels

See how fast it feelsinternlm2 limarp chat 20b on RTX PRO 6000 Blackwell Server Edition 96GB
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: 110.0 tok/s decode · 1.8s TTFT (warm) · 275 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 well110.0 tok/s960 ms498K
CodingCRuns well110.0 tok/s1761 ms498K
Agentic CodingCRuns well110.0 tok/s2561 ms498K
ReasoningCRuns well110.0 tok/s2081 ms498K
RAGCRuns well110.0 tok/s3201 ms498K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowD39
Q3_K_S
3
9.8 GB
LowD39
NVFP4
4
11.2 GB
MediumD39
Q4_K_M
4
12.2 GB
MediumD39
Q5_K_M
5
14.4 GB
HighD39
Q6_K
6
16.4 GB
HighD40
Q8_0
8
21.4 GB
Very HighC40
F16Best for your GPU
16
41.0 GB
MaximumC44

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 6000 Blackwell Server Edition 96GB run internlm2 limarp chat 20b?

Yes, RTX PRO 6000 Blackwell Server Edition 96GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 110.0 tok/s.

How much VRAM does internlm2 limarp chat 20b need?

internlm2 limarp chat 20b (20B parameters) requires approximately 25.3 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 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, internlm2 limarp chat 20b achieves approximately 110.0 tokens per second decode speed with a time-to-first-token of 1761ms using Q4_K_M quantization.

Can RTX PRO 6000 Blackwell Server Edition 96GB run internlm2 limarp chat 20b for coding?

For coding workloads, internlm2 limarp chat 20b on RTX PRO 6000 Blackwell Server Edition 96GB receives a C grade with 110.0 tok/s and 498K context.

What context window can internlm2 limarp chat 20b use on RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, internlm2 limarp chat 20b can safely use up to 498K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX PRO 6000 Blackwell Server Edition 96GBSee all hardware for internlm2 limarp chat 20b
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