Can internlm2 math plus 20b i1 run on NVIDIA A100 40GB?

YES — Runs Great

C52Usable

Estimated from fit model

internlm2 math plus 20b i1 needs ~19.7 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~107 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) — 19.7 GB, 107.1 tok/s, Runs well

19.7 GB required40.0 GB available

49% VRAM used

Fit status

Runs well

Decode

107.1 tok/s

TTFT

1808 ms

Safe context

154K

Memory

19.7 GB / 40.0 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 20b i1 on NVIDIA A100 40GB
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: 107.1 tok/s decode · 1.8s TTFT (warm) · 268 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	107.1 tok/s	986 ms	154K
Coding	C	Runs well	107.1 tok/s	1808 ms	154K
Agentic Coding	C	Runs well	107.1 tok/s	2630 ms	154K
Reasoning	C	Runs well	107.1 tok/s	2137 ms	154K
RAG	C	Runs well	107.1 tok/s	3288 ms	154K

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C43
Q3_K_S	3	9.8 GB	Low	C44
NVFP4	4	11.2 GB	Medium	C44
Q4_K_M	4	12.2 GB	Medium	C44
Q5_K_M	5	14.4 GB	High	C45
Q6_K	6	16.4 GB	High	C46
Q8_0Best for your GPU	8	21.4 GB	Very High	C48
F16	16	41.0 GB	Maximum	F0

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

Can NVIDIA A100 40GB run internlm2 math plus 20b i1?

Yes, NVIDIA A100 40GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 107.1 tok/s.

How much VRAM does internlm2 math plus 20b i1 need?

internlm2 math plus 20b i1 (20B parameters) requires approximately 19.7 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm2 math plus 20b i1?

The recommended quantization for internlm2 math plus 20b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm2 math plus 20b i1 run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, internlm2 math plus 20b i1 achieves approximately 107.1 tokens per second decode speed with a time-to-first-token of 1808ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on NVIDIA A100 40GB receives a C grade with 107.1 tok/s and 154K context.

What context window can internlm2 math plus 20b i1 use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, internlm2 math plus 20b i1 can safely use up to 154K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for internlm2 math plus 20b i1

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