Can internlm2 limarp chat 20b run on Gaudi 3 128GB?

YES — Runs Great

C46Usable

Estimated from fit model

internlm2 limarp chat 20b needs ~28.2 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~212 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 28.2 GB, 212.3 tok/s, Runs well

28.2 GB required128.0 GB available

22% VRAM used

Fit status

Runs well

Decode

212.3 tok/s

TTFT

912 ms

Safe context

697K

Memory

28.2 GB / 128.0 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsinternlm2 limarp chat 20b on Gaudi 3 128GB
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: 212.3 tok/s decode · 912ms TTFT (warm) · 531 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	212.3 tok/s	497 ms	697K
Coding	C	Runs well	212.3 tok/s	912 ms	697K
Agentic Coding	C	Runs well	212.3 tok/s	1326 ms	697K
Reasoning	C	Runs well	212.3 tok/s	1078 ms	697K
RAG	C	Runs well	212.3 tok/s	1658 ms	697K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	D38
Q3_K_S	3	9.8 GB	Low	D38
NVFP4	4	11.2 GB	Medium	D38
Q4_K_M	4	12.2 GB	Medium	D38
Q5_K_M	5	14.4 GB	High	D38
Q6_K	6	16.4 GB	High	D38
Q8_0	8	21.4 GB	Very High	D39
F16Best for your GPU	16	41.0 GB	Maximum	C42

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 Gaudi 3 128GB run internlm2 limarp chat 20b?

Yes, Gaudi 3 128GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 212.3 tok/s.

How much VRAM does internlm2 limarp chat 20b need?

internlm2 limarp chat 20b (20B parameters) requires approximately 28.2 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 Gaudi 3 128GB?

On Gaudi 3 128GB, internlm2 limarp chat 20b achieves approximately 212.3 tokens per second decode speed with a time-to-first-token of 912ms using Q4_K_M quantization.

Can Gaudi 3 128GB run internlm2 limarp chat 20b for coding?

For coding workloads, internlm2 limarp chat 20b on Gaudi 3 128GB receives a C grade with 212.3 tok/s and 697K context.

What context window can internlm2 limarp chat 20b use on Gaudi 3 128GB?

On Gaudi 3 128GB, internlm2 limarp chat 20b can safely use up to 697K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if internlm2 limarp chat 20b feels slow on Gaudi 3 128GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Gaudi 3 128GB for internlm2 limarp chat 20b?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Gaudi 3 128GB See all hardware for internlm2 limarp chat 20b

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<iframe src="https://willitrunai.com/embed/hf-intervitens-archive--internlm2-limarp-chat-20b-gguf-on-gaudi-3-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

212.3 tok/s

497 ms

697K

Coding

Runs well

212.3 tok/s

912 ms

697K

Agentic Coding

Runs well

212.3 tok/s

1326 ms

697K

Reasoning

Runs well

212.3 tok/s

1078 ms

697K

RAG

Runs well

212.3 tok/s

1658 ms

697K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	D38
Q3_K_S	3	9.8 GB	Low	D38
NVFP4	4	11.2 GB	Medium	D38
Q4_K_M	4	12.2 GB	Medium	D38
Q5_K_M	5	14.4 GB	High	D38
Q6_K	6	16.4 GB	High	D38
Q8_0	8	21.4 GB	Very High	D39
F16Best for your GPU	16	41.0 GB	Maximum	C42

Frequently asked questions

Can Gaudi 3 128GB run internlm2 limarp chat 20b?

Yes, Gaudi 3 128GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 212.3 tok/s.

How much VRAM does internlm2 limarp chat 20b need?

internlm2 limarp chat 20b (20B parameters) requires approximately 28.2 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 Gaudi 3 128GB?

On Gaudi 3 128GB, internlm2 limarp chat 20b achieves approximately 212.3 tokens per second decode speed with a time-to-first-token of 912ms using Q4_K_M quantization.

Can Gaudi 3 128GB run internlm2 limarp chat 20b for coding?

For coding workloads, internlm2 limarp chat 20b on Gaudi 3 128GB receives a C grade with 212.3 tok/s and 697K context.

What context window can internlm2 limarp chat 20b use on Gaudi 3 128GB?

On Gaudi 3 128GB, internlm2 limarp chat 20b can safely use up to 697K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if internlm2 limarp chat 20b feels slow on Gaudi 3 128GB?

Would CUDA be a better path than Gaudi 3 128GB for internlm2 limarp chat 20b?