Can internlm2 math plus 20b i1 run on RTX 4000 Ada 20GB?

YES — Tight Fit

C49Usable

Estimated from fit model

internlm2 math plus 20b i1 needs ~17.7 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 17.7 GB, 23.0 tok/s, Tight fit

17.7 GB required20.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

23.0 tok/s

TTFT

8411 ms

Safe context

31K

Memory

17.7 GB / 20.0 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 20b i1 on RTX 4000 Ada 20GB
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: 23.0 tok/s decode · 8.4s TTFT (warm) · 58 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	Tight fit	23.0 tok/s	4588 ms	31K
Coding	C	Tight fit	23.0 tok/s	8411 ms	31K
Agentic Coding	C	Runs with offload (needs ~0.1 GB host RAM)	17.1 tok/s	16464 ms	31K
Reasoning	C	Tight fit	23.0 tok/s	9941 ms	31K
RAG	C	Runs with offload (needs ~0.1 GB host RAM)	17.1 tok/s	20580 ms

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C49
Q3_K_S	3	9.8 GB	Low	C50
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

RTX 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+576)

Raises estimated decode speed by about 133%.53.7 tok/s decode

Raises estimated decode speed by about 133%.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+648)

Raises estimated decode speed by about 173%.62.8 tok/s decode

Raises estimated decode speed by about 173%.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+4)672 GB/s (+312)

Raises estimated decode speed by about 101%.46.3 tok/s decode

Raises estimated decode speed by about 101%.

~$1,599 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run internlm2 math plus 20b i1?

Yes, RTX 4000 Ada 20GB can run internlm2 math plus 20b i1 with a C grade (Tight fit). Expected decode speed: 23.0 tok/s.

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

internlm2 math plus 20b i1 (20B parameters) requires approximately 17.7 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 RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, internlm2 math plus 20b i1 achieves approximately 23.0 tokens per second decode speed with a time-to-first-token of 8411ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on RTX 4000 Ada 20GB receives a C grade with 23.0 tok/s and 31K context.

What context window can internlm2 math plus 20b i1 use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, internlm2 math plus 20b i1 can safely use up to 31K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for internlm2 math plus 20b i1

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