Can internlm2 math plus 20b i1 run on RTX 3500 Ada Laptop 12GB?

Q: What should I upgrade first if internlm2 math plus 20b i1 feels slow on RTX 3500 Ada Laptop 12GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Q2_K

C48Usable

Estimated from fit model

internlm2 math plus 20b i1 needs ~12.5 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q2_K quantization, expect ~18 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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.

internlm2 math plus 20b i1 at Q4_K_M needs 16.9 GB — too much for RTX 3500 Ada Laptop 12GB (12.0 GB). Runs at Q2_K (12.5 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 16.9 GB, exceeds 12.0 GB available

16.9 GB required12.0 GB available

141% VRAM needed

4.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.3 tok/s

TTFT

26542 ms

Safe context

Memory

16.9 GB / 12.0 GB

Offload

30%

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsinternlm2 math plus 20b i1 on RTX 3500 Ada Laptop 12GB
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: 7.3 tok/s decode · 26.5s TTFT (warm) · 18 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	8.5 tok/s	12450 ms	4K
Coding	F	Too heavy	7.3 tok/s	26542 ms	4K
Agentic Coding	F	Too heavy	5.6 tok/s	50711 ms	4K
Reasoning	F	Too heavy	7.3 tok/s	31368 ms	4K
RAG	F	Too heavy	5.6 tok/s	63388 ms	4K

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	7.8 GB	Low	C51
Q3_K_S	3	9.8 GB	Low	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

Upgrade options

Hardware that runs internlm2 math plus 20b i1 well

RTX 5060 Ti 16GBBest value

16 GB VRAM (+4)448 GB/s (+112)

Makes the model fit on the accelerator instead of staying completely out of reach.14.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 103%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.10.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 49%.

~$499 MSRP

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+8)360 GB/s (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.23 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,250 MSRP

RTX 4090 24GBBiggest leap

24 GB VRAM (+12)1008 GB/s (+672)

Makes the model fit on the accelerator instead of staying completely out of reach.62.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run internlm2 math plus 20b i1?

Yes, RTX 3500 Ada Laptop 12GB can run internlm2 math plus 20b i1 at Q2_K quantization (Runs with offload (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 16.9 GB which exceeds available memory, but at Q2_K it needs only 12.5 GB. Expected decode speed: 18.3 tok/s.

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

internlm2 math plus 20b i1 (20B parameters) requires approximately 16.9 GB at Q4_K_M quantization. On RTX 3500 Ada Laptop 12GB, it fits at Q2_K using 12.5 GB.

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

The recommended quantization is Q4_K_M, but on RTX 3500 Ada Laptop 12GB the best fitting quantization is Q2_K, which uses 12.5 GB.

What speed will internlm2 math plus 20b i1 run at on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, internlm2 math plus 20b i1 achieves approximately 18.3 tokens per second decode speed with a time-to-first-token of 10602ms using Q2_K quantization.

Can RTX 3500 Ada Laptop 12GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on RTX 3500 Ada Laptop 12GB receives a F grade with 7.3 tok/s and 4K context.

What context window can internlm2 math plus 20b i1 use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, internlm2 math plus 20b i1 can safely use up to 12K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3500 Ada Laptop 12GB See all hardware for internlm2 math plus 20b i1

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