Can CogVLM2 19B run on RTX 3080 10GB?

YES — With Q2_K

A74Great

Estimated from fit model

CogVLM2 19B needs ~11.8 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q2_K quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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.

CogVLM2 19B at Q4_K_M needs 15.9 GB — too much for RTX 3080 10GB (10.0 GB). Runs at Q2_K (11.8 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 15.9 GB, exceeds 10.0 GB available

15.9 GB required10.0 GB available

159% VRAM needed

5.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.5 tok/s

TTFT

14331 ms

Safe context

Memory

15.9 GB / 10.0 GB

Offload

40%

Memory breakdown

Weights11.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCogVLM2 19B on RTX 3080 10GB
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: 13.5 tok/s decode · 14.3s TTFT (warm) · 34 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 10% 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 1.1 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	F	Too heavy	16.0 tok/s	6609 ms	4K
Coding	F	Too heavy	13.5 tok/s	14331 ms	4K
Agentic Coding	F	Too heavy	10.0 tok/s	28142 ms	4K
Reasoning	F	Too heavy	13.5 tok/s	16937 ms	4K
RAG	F	Too heavy	10.0 tok/s	35177 ms	4K

Quantization options

How CogVLM2 19B (19B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	F0
Q3_K_S	3	9.3 GB	Low	F0
NVFP4	4	10.6 GB	Medium	F0
Q4_K_M	4	11.6 GB	Medium	F0
Q5_K_M	5	13.7 GB	High	F0
Q6_K	6	15.6 GB	High	F0
Q8_0	8	20.3 GB	Very High	F0
F16	16	38.9 GB	Maximum	F0

Get started

Copy-paste commands to run CogVLM2 19B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "THUDM/cogvlm2-llama3-chat-19B" \
  --hf-file "cogvlm2-llama3-chat-19B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Opciones de mejora

Hardware que ejecuta bien CogVLM2 19B

RTX 5060 Ti 16GBOpción económica

16 GB VRAM (+6)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.17.3 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$449 MSRP

RTX 4060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+6)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.11.4 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$499 MSRP

RTX 2000 Ada 16GBMejora NVIDIA

16 GB VRAM (+6)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.14.2 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$625 MSRP

RTX 5090 Laptop 24GBMayor salto

24 GB VRAM (+14)896 GB/s (+136)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.69.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Frequently asked questions

Can RTX 3080 10GB run CogVLM2 19B?

Yes, RTX 3080 10GB can run CogVLM2 19B at Q2_K quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q4_K_M requires 15.9 GB which exceeds available memory, but at Q2_K it needs only 11.8 GB. Expected decode speed: 34.1 tok/s.

How much VRAM does CogVLM2 19B need?

CogVLM2 19B (19B parameters) requires approximately 15.9 GB at Q4_K_M quantization. On RTX 3080 10GB, it fits at Q2_K using 11.8 GB.

What is the best quantization for CogVLM2 19B?

The recommended quantization is Q4_K_M, but on RTX 3080 10GB the best fitting quantization is Q2_K, which uses 11.8 GB.

What speed will CogVLM2 19B run at on RTX 3080 10GB?

On RTX 3080 10GB, CogVLM2 19B achieves approximately 34.1 tokens per second decode speed with a time-to-first-token of 5681ms using Q2_K quantization.

Can RTX 3080 10GB run CogVLM2 19B for coding?

For coding workloads, CogVLM2 19B on RTX 3080 10GB receives a F grade with 13.5 tok/s and 4K context.

What context window can CogVLM2 19B use on RTX 3080 10GB?

On RTX 3080 10GB, CogVLM2 19B can safely use up to 5K tokens of context at Q2_K quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if CogVLM2 19B feels slow on RTX 3080 10GB?

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 10GB See all hardware for CogVLM2 19B

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