Can CogVLM2 19B run on RTX 4000 Ada 20GB?

YES — Tight Fit

A83Great

Estimated from fit model

CogVLM2 19B needs ~16.9 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: 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) — 16.9 GB, 26.0 tok/s, Tight fit

16.9 GB required20.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

26.0 tok/s

TTFT

7433 ms

Safe context

Memory

16.9 GB / 20.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsCogVLM2 19B on RTX 4000 Ada 20GB
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: 26.0 tok/s decode · 7.4s TTFT (warm) · 65 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	S	Runs well	26.0 tok/s	4055 ms	8K
Coding	A	Tight fit	26.0 tok/s	7433 ms	8K
Agentic Coding	A	Runs with offload	26.0 tok/s	10812 ms	8K
Reasoning	A	Tight fit	26.0 tok/s	8785 ms	8K
RAG	A	Runs with offload	26.0 tok/s	13515 ms	8K

Quantization options

How CogVLM2 19B (19B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	A82
Q3_K_S	3	9.3 GB	Low	A84
NVFP4	4	10.6 GB	Medium	A84
Q4_K_M	4	11.6 GB	Medium	A84
Q5_K_M	5	13.7 GB	High	A83
Q6_KBest for your GPU	6	15.6 GB	High	A83
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

Your hardware

More models your RTX 4000 Ada 20GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	23.8 tok/s
Qwen 3.5 27B	27B	A	10.7 tok/s
Qwen 3.6 27B	27B	S	10.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	25.3 tok/s
Magistral Small 2507	24B	S	20.6 tok/s

Frequently asked questions

Can RTX 4000 Ada 20GB run CogVLM2 19B?

Yes, RTX 4000 Ada 20GB can run CogVLM2 19B with a A grade (Tight fit). Expected decode speed: 26.0 tok/s.

How much VRAM does CogVLM2 19B need?

CogVLM2 19B (19B parameters) requires approximately 16.9 GB of memory with Q4_K_M quantization.

What is the best quantization for CogVLM2 19B?

The recommended quantization for CogVLM2 19B is Q4_K_M, which balances quality and memory efficiency.

What speed will CogVLM2 19B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, CogVLM2 19B achieves approximately 26.0 tokens per second decode speed with a time-to-first-token of 7433ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run CogVLM2 19B for coding?

For coding workloads, CogVLM2 19B on RTX 4000 Ada 20GB receives a A grade with 26.0 tok/s and 8K context.

What context window can CogVLM2 19B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, CogVLM2 19B 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 CogVLM2 19B

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