Can InternLM 7B run on RTX 3080 12GB?

BARELY — Tight on Memory

B58Good

Estimated from fit model

InternLM 7B needs ~14.2 GB VRAM. RTX 3080 12GB has 12.0 GB. With Q4_K_M quantization, expect ~72 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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) — 14.2 GB, 71.9 tok/s, Very compromised (needs ~0.7 GB host RAM)

14.2 GB required12.0 GB available

118% VRAM needed

2.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.7 GB host RAM)

Decode

71.9 tok/s

TTFT

2691 ms

Safe context

Memory

14.2 GB / 12.0 GB

Offload

20%

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsInternLM 7B on RTX 3080 12GB
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: 71.9 tok/s decode · 2.7s TTFT (warm) · 180 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	84.0 tok/s	1257 ms	8K
Coding	B	Very compromised (needs ~0.7 GB host RAM)	71.9 tok/s	2691 ms	8K
Agentic Coding	F	Too heavy	28.6 tok/s	9859 ms	8K
Reasoning	B	Very compromised (needs ~0.7 GB host RAM)	71.9 tok/s	3181 ms	8K
RAG	F	Too heavy	28.6 tok/s	12324 ms	8K

Quantization options

How InternLM 7B (7B params) fits at each quantization level on RTX 3080 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B70
Q3_K_S	3	3.4 GB	Low	A70
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

Upgrade options

Hardware that runs InternLM 7B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.65 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.45.3 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.51.3 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$625 MSRP

Frequently asked questions

Can RTX 3080 12GB run InternLM 7B?

Yes, RTX 3080 12GB can run InternLM 7B with a B grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 71.9 tok/s.

How much VRAM does InternLM 7B need?

InternLM 7B (7B parameters) requires approximately 14.2 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 3080 12GB?

On RTX 3080 12GB, InternLM 7B achieves approximately 71.9 tokens per second decode speed with a time-to-first-token of 2691ms using Q4_K_M quantization.

Can RTX 3080 12GB run InternLM 7B for coding?

For coding workloads, InternLM 7B on RTX 3080 12GB receives a B grade with 71.9 tok/s and 8K context.

What context window can InternLM 7B use on RTX 3080 12GB?

On RTX 3080 12GB, 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 3080 12GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 3080 12GB See all hardware for InternLM 7B

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