Can InternLM 7B run on RTX 2000 Ada 16GB?

YES — Tight Fit

A73Great

○Estimated from fit model

InternLM 7B needs ~14.9 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~51 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) — 14.9 GB, 51.3 tok/s, Tight fit

14.9 GB required16.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

51.3 tok/s

TTFT

3777 ms

Safe context

Memory

14.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsInternLM 7B on RTX 2000 Ada 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 51.3 tok/s decode · 3.8s TTFT (warm) · 128 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	A	Runs well	51.3 tok/s	2060 ms	8K
Coding	A	Tight fit	51.3 tok/s	3777 ms	8K
Agentic Coding	F	Too heavy	18.4 tok/s	15288 ms	8K
Reasoning	A	Tight fit	51.3 tok/s	4464 ms	8K
RAG	F	Too heavy	18.4 tok/s	19110 ms	8K

Quantization options

How InternLM 7B (7B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B67
Q3_K_S	3	3.4 GB	Low	B68
NVFP4	4	3.9 GB	Medium	B68
Q4_K_M	4	4.3 GB	Medium	B69
Q5_K_M	5	5.0 GB	High	B69
Q6_K	6	5.7 GB	High	A70
Q8_0Best for your GPU	8	7.5 GB	Very High	A72
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run InternLM 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "InternLM/InternLM-7B" \
 --hf-file "InternLM-7B-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your RTX 2000 Ada 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	42.9 tok/s
👁 Alibaba Qwen 3 14B	14B	S	27.7 tok/s
👁 Alibaba Qwen 3 8B	8B	S	48.2 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	26.2 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	24.4 tok/s

Frequently asked questions

See all results for RTX 2000 Ada 16GB See all hardware for InternLM 7B

URL: https://willitrunai.com/can-run/internlm-7b-on-rtx-2000-ada-16gb

⇱ InternLM 7B on RTX 2000 Ada 16GB? TIGHT FIT