Can InternLM 7B run on RTX 5080 16GB?

YES — Tight Fit

A75Great

Estimated from fit model

InternLM 7B needs ~14.9 GB VRAM. RTX 5080 16GB has 16.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: TightBandwidth: HighStack: 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, 98.0 tok/s, Tight fit

14.9 GB required16.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

98.0 tok/s

TTFT

1976 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 5080 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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	98.0 tok/s	1078 ms	8K
Coding	A	Tight fit	98.0 tok/s	1976 ms	8K
Agentic Coding	F	Too heavy	54.8 tok/s	5141 ms	8K
Reasoning	A	Tight fit	98.0 tok/s	2335 ms	8K
RAG	F	Too heavy	54.8 tok/s	6427 ms	8K

Quantization options

How InternLM 7B (7B params) fits at each quantization level on RTX 5080 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

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 5080 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	122.2 tok/s
Qwen 3 14B	14B	S	78.9 tok/s

Frequently asked questions

Can RTX 5080 16GB run InternLM 7B?

Yes, RTX 5080 16GB can run InternLM 7B with a A grade (Tight fit). Expected decode speed: 98.0 tok/s.

How much VRAM does InternLM 7B need?

InternLM 7B (7B parameters) requires approximately 14.9 GB of memory with Q4_K_M quantization.

What is the best quantization for InternLM 7B?

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

What speed will InternLM 7B run at on RTX 5080 16GB?

On RTX 5080 16GB, InternLM 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can RTX 5080 16GB run InternLM 7B for coding?

For coding workloads, InternLM 7B on RTX 5080 16GB receives a A grade with 98.0 tok/s and 8K context.

What context window can InternLM 7B use on RTX 5080 16GB?

On RTX 5080 16GB, InternLM 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.

What should I upgrade first if InternLM 7B feels slow on RTX 5080 16GB?

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

See all results for RTX 5080 16GB See all hardware for InternLM 7B

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