Can InternLM Chat 7B run on RTX 4000 Ada 20GB?

YES — Runs Great

A76Great

Estimated from fit model

InternLM Chat 7B needs ~15.3 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~66 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 15.3 GB, 65.8 tok/s, Runs well

15.3 GB required20.0 GB available

77% VRAM used

Fit status

Runs well

Decode

65.8 tok/s

TTFT

2944 ms

Safe context

Memory

15.3 GB / 20.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsInternLM Chat 7B on RTX 4000 Ada 20GB
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: 65.8 tok/s decode · 2.9s TTFT (warm) · 164 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	A	Runs well	65.8 tok/s	1606 ms	8K
Coding	A	Runs well	65.8 tok/s	2944 ms	8K
Agentic Coding	B	Very compromised	36.4 tok/s	7729 ms	8K
Reasoning	A	Runs well	65.8 tok/s	3479 ms	8K
RAG	B	Very compromised	36.4 tok/s	9662 ms	8K

Quantization options

How InternLM Chat 7B (7B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B66
Q3_K_S	3	3.4 GB	Low	B66
NVFP4	4

Get started

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

Run

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

Your hardware

More models your RTX 4000 Ada 20GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	23.2 tok/s
Qwen 3.5 27B	27B	A	10.4 tok/s

Frequently asked questions

Can RTX 4000 Ada 20GB run InternLM Chat 7B?

Yes, RTX 4000 Ada 20GB can run InternLM Chat 7B with a A grade (Runs well). Expected decode speed: 65.8 tok/s.

How much VRAM does InternLM Chat 7B need?

InternLM Chat 7B (7B parameters) requires approximately 15.3 GB of memory with Q4_K_M quantization.

What is the best quantization for InternLM Chat 7B?

The recommended quantization for InternLM Chat 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will InternLM Chat 7B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, InternLM Chat 7B achieves approximately 65.8 tokens per second decode speed with a time-to-first-token of 2944ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run InternLM Chat 7B for coding?

For coding workloads, InternLM Chat 7B on RTX 4000 Ada 20GB receives a A grade with 65.8 tok/s and 8K context.

What context window can InternLM Chat 7B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, InternLM Chat 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for InternLM Chat 7B

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