Can internlm2 math plus 20b i1 run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

C48Usable

○Estimated from fit model

internlm2 math plus 20b i1 needs ~23.7 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~138 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 23.7 GB, 137.7 tok/s, Runs well

23.7 GB required80.0 GB available

30% VRAM used

Fit status

Runs well

Decode

137.7 tok/s

TTFT

1406 ms

Safe context

400K

Memory

23.7 GB / 80.0 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 20b i1 on NVIDIA H100 PCIe 80GB
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: 137.7 tok/s decode · 1.4s TTFT (warm) · 344 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	137.7 tok/s	767 ms	400K
Coding	C	Runs well	137.7 tok/s	1406 ms	400K
Agentic Coding	C	Runs well	137.7 tok/s	2045 ms	400K
Reasoning	C	Runs well	137.7 tok/s	1662 ms	400K
RAG	C	Runs well	137.7 tok/s	2556 ms	400K

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	D39
Q3_K_S	3	9.8 GB	Low	D40
NVFP4	4

Get started

Copy-paste commands to run internlm2 math plus 20b i1 on your machine.

Run

lms load hf-mradermacher--internlm2-math-plus-20b-i1-gguf && lms server start

Frequently asked questions

See all results for NVIDIA H100 PCIe 80GB See all hardware for internlm2 math plus 20b i1

URL: https://willitrunai.com/can-run/hf-mradermacher--internlm2-math-plus-20b-i1-gguf-on-h100-pcie-80gb

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