Will It Run AI

Can internlm2 limarp chat 20b run on AMD Instinct MI300A 128GB?

YES — Runs Great

C46Usable
Estimated from fit model

internlm2 limarp chat 20b needs ~28.2 GB VRAM. AMD Instinct MI300A 128GB has 128.0 GB. With Q4_K_M quantization, expect ~280 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:

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, 280.0 tok/s, Runs well
28.2 GB required128.0 GB available
22% VRAM used

Fit status

Runs well

Decode

280.0 tok/s

TTFT

691 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 AMD Instinct MI300A 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: 280.0 tok/s decode · 691ms TTFT (warm) · 700 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 well280.0 tok/s377 ms697K
CodingCRuns well280.0 tok/s691 ms697K
Agentic CodingCRuns well280.0 tok/s1006 ms697K
ReasoningCRuns well280.0 tok/s817 ms697K
RAGCRuns well280.0 tok/s1257 ms697K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on AMD Instinct MI300A 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowD38
Q3_K_S
3
9.8 GB
LowD38
NVFP4
4
11.2 GB
MediumD38
Q4_K_M
4
12.2 GB
MediumD38
Q5_K_M
5
14.4 GB
HighD38
Q6_K
6
16.4 GB
HighD38
Q8_0
8
21.4 GB
Very HighD39
F16Best for your GPU
16
41.0 GB
MaximumC42

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 AMD Instinct MI300A 128GB run internlm2 limarp chat 20b?

Yes, AMD Instinct MI300A 128GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 280.0 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 AMD Instinct MI300A 128GB?

On AMD Instinct MI300A 128GB, internlm2 limarp chat 20b achieves approximately 280.0 tokens per second decode speed with a time-to-first-token of 691ms using Q4_K_M quantization.

Can AMD Instinct MI300A 128GB run internlm2 limarp chat 20b for coding?

For coding workloads, internlm2 limarp chat 20b on AMD Instinct MI300A 128GB receives a C grade with 280.0 tok/s and 697K context.

What context window can internlm2 limarp chat 20b use on AMD Instinct MI300A 128GB?

On AMD Instinct MI300A 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.

See all results for AMD Instinct MI300A 128GBSee all hardware for internlm2 limarp chat 20b
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-intervitens-archive--internlm2-limarp-chat-20b-gguf-on-instinct-mi300a-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: