Can internlm2 math plus 20b i1 run on RTX 5000 Ada 32GB?

YES — Runs Great

C51Usable

Estimated from fit model

internlm2 math plus 20b i1 needs ~18.9 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 18.9 GB, 37.8 tok/s, Runs well

18.9 GB required32.0 GB available

59% VRAM used

Fit status

Runs well

Decode

37.8 tok/s

TTFT

5126 ms

Safe context

105K

Memory

18.9 GB / 32.0 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 20b i1 on RTX 5000 Ada 32GB
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: 37.8 tok/s decode · 5.1s TTFT (warm) · 94 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	37.8 tok/s	2796 ms	105K
Coding	C	Runs well	37.8 tok/s	5126 ms	105K
Agentic Coding	C	Runs well	37.8 tok/s	7456 ms	105K
Reasoning	C	Runs well	37.8 tok/s	6058 ms	105K
RAG	C	Runs well	37.8 tok/s	9319 ms	105K

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C44
Q3_K_S	3	9.8 GB	Low	C45
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

Upgrade options

Hardware that runs internlm2 math plus 20b i1 well

MacBook Pro M4 Max 48GBBest value

48 GB Unified (+16)

This setup is broadly balanced for this model.35.6 tok/s decode

~$2,499 MSRP

NVIDIA A100 40GBBudget pick

40 GB VRAM (+8)1555 GB/s (+979)

Raises estimated decode speed by about 183%.107.1 tok/s decode

Raises estimated decode speed by about 183%.

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

~$10,000 MSRP

Frequently asked questions

Can RTX 5000 Ada 32GB run internlm2 math plus 20b i1?

Yes, RTX 5000 Ada 32GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 37.8 tok/s.

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

internlm2 math plus 20b i1 (20B parameters) requires approximately 18.9 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 RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, internlm2 math plus 20b i1 achieves approximately 37.8 tokens per second decode speed with a time-to-first-token of 5126ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on RTX 5000 Ada 32GB receives a C grade with 37.8 tok/s and 105K context.

What context window can internlm2 math plus 20b i1 use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, internlm2 math plus 20b i1 can safely use up to 105K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for internlm2 math plus 20b i1

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