Can internlm2 limarp chat 20b run on Radeon PRO W7900 DS 48GB?

YES — Runs Great

C48Usable

○Estimated from fit model

internlm2 limarp chat 20b needs ~20.2 GB VRAM. Radeon PRO W7900 DS 48GB has 48.0 GB. With Q4_K_M quantization, expect ~42 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) — 20.2 GB, 41.8 tok/s, Runs well

20.2 GB required48.0 GB available

42% VRAM used

Fit status

Runs well

Decode

41.8 tok/s

TTFT

4633 ms

Safe context

205K

Memory

20.2 GB / 48.0 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsinternlm2 limarp chat 20b on Radeon PRO W7900 DS 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: 41.8 tok/s decode · 4.6s TTFT (warm) · 105 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	41.8 tok/s	2527 ms	205K
Coding	C	Runs well	41.8 tok/s	4633 ms	205K
Agentic Coding	C	Runs well	41.8 tok/s	6739 ms	205K
Reasoning	C	Runs well	41.8 tok/s	5476 ms	205K
RAG	C	Runs well	41.8 tok/s	8424 ms	205K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on Radeon PRO W7900 DS 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

Upgrade options

Hardware that runs internlm2 limarp chat 20b well

AMD Instinct MI210 64GBBudget pick

64 GB VRAM (+16)1638 GB/s (+774)

Raises estimated decode speed by about 118%.91.3 tok/s decode

Raises estimated decode speed by about 118%.

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

~$10,000 MSRP

Frequently asked questions

See all results for Radeon PRO W7900 DS 48GB See all hardware for internlm2 limarp chat 20b

URL: https://willitrunai.com/can-run/hf-intervitens-archive--internlm2-limarp-chat-20b-gguf-on-radeon-pro-w7900-ds-48gb

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