Will It Run AI

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

YES — Runs Great

C48Usable
Estimated from fit model

internlm2 limarp chat 20b needs ~20.2 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 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 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well41.8 tok/s2527 ms205K
CodingCRuns well41.8 tok/s4633 ms205K
Agentic CodingCRuns well41.8 tok/s6739 ms205K
ReasoningCRuns well41.8 tok/s5476 ms205K
RAGCRuns well41.8 tok/s8424 ms205K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowC42
Q3_K_S
3
9.8 GB
LowC42
NVFP4
4
11.2 GB
MediumC43
Q4_K_M
4
12.2 GB
MediumC43
Q5_K_M
5
14.4 GB
HighC44
Q6_K
6
16.4 GB
HighC44
Q8_0
8
21.4 GB
Very HighC46
F16Best for your GPU
16
41.0 GB
MaximumC47

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-Optionen

Hardware, die internlm2 limarp chat 20b gut ausführt

AMD Instinct MI210 64GBGünstige Wahl
64 GB VRAM (+16)1638 GB/s (+774)
C
Raises estimated decode speed by about 118%.91.3 tok/s Dekodierung

Raises estimated decode speed by about 118%.

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

ca. $10,000 MSRP

Frequently asked questions

Can Radeon Pro W7900 48GB run internlm2 limarp chat 20b?

Yes, Radeon Pro W7900 48GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 41.8 tok/s.

How much VRAM does internlm2 limarp chat 20b need?

internlm2 limarp chat 20b (20B parameters) requires approximately 20.2 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 Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, internlm2 limarp chat 20b achieves approximately 41.8 tokens per second decode speed with a time-to-first-token of 4633ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run internlm2 limarp chat 20b for coding?

For coding workloads, internlm2 limarp chat 20b on Radeon Pro W7900 48GB receives a C grade with 41.8 tok/s and 205K context.

What context window can internlm2 limarp chat 20b use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, internlm2 limarp chat 20b can safely use up to 205K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7900 48GBSee all hardware for internlm2 limarp chat 20b
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