Can CogVLM2 19B run on RX 6750 XT 12GB?

Q: What should I upgrade first if CogVLM2 19B feels slow on RX 6750 XT 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.

YES — With Q3_K_S

A71Great

Estimated from fit model

CogVLM2 19B needs ~13.9 GB VRAM. RX 6750 XT 12GB has 12.0 GB. With Q3_K_S quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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 16.1 GB — too much for RX 6750 XT 12GB (12.0 GB). Runs at Q3_K_S (13.9 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 16.1 GB, exceeds 12.0 GB available

16.1 GB required12.0 GB available

134% VRAM needed

4.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.5 tok/s

TTFT

22659 ms

Safe context

Memory

16.1 GB / 12.0 GB

Offload

30%

Memory breakdown

Weights11.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCogVLM2 19B on RX 6750 XT 12GB
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: 8.5 tok/s decode · 22.7s TTFT (warm) · 21 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 1.2 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	10.1 tok/s	10473 ms	4K
Coding	F	Too heavy	8.5 tok/s	22659 ms	4K
Agentic Coding	F	Too heavy	6.4 tok/s	44340 ms	4K
Reasoning	F	Too heavy	8.5 tok/s	26778 ms	4K
RAG	F	Too heavy	6.4 tok/s	55425 ms	4K

Quantization options

How CogVLM2 19B (19B params) fits at each quantization level on RX 6750 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	7.4 GB	Low	A85
Q3_K_S	3	9.3 GB	Low	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

Upgrade options

Hardware that runs CogVLM2 19B well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.10.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.13.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$349 MSRP

RX 9070 16GBAMD upgrade

16 GB VRAM (+4)640 GB/s (+208)

Makes the model fit on the accelerator instead of staying completely out of reach.26.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$479 MSRP

RTX 5090 Laptop 24GBBiggest leap

24 GB VRAM (+12)896 GB/s (+464)

Makes the model fit on the accelerator instead of staying completely out of reach.69.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

Frequently asked questions

Can RX 6750 XT 12GB run CogVLM2 19B?

Yes, RX 6750 XT 12GB can run CogVLM2 19B at Q3_K_S quantization (Very compromised (needs ~1.2 GB host RAM)). The recommended Q4_K_M requires 16.1 GB which exceeds available memory, but at Q3_K_S it needs only 13.9 GB. Expected decode speed: 13.6 tok/s.

How much VRAM does CogVLM2 19B need?

CogVLM2 19B (19B parameters) requires approximately 16.1 GB at Q4_K_M quantization. On RX 6750 XT 12GB, it fits at Q3_K_S using 13.9 GB.

What is the best quantization for CogVLM2 19B?

The recommended quantization is Q4_K_M, but on RX 6750 XT 12GB the best fitting quantization is Q3_K_S, which uses 13.9 GB.

What speed will CogVLM2 19B run at on RX 6750 XT 12GB?

On RX 6750 XT 12GB, CogVLM2 19B achieves approximately 13.6 tokens per second decode speed with a time-to-first-token of 14201ms using Q3_K_S quantization.

Can RX 6750 XT 12GB run CogVLM2 19B for coding?

For coding workloads, CogVLM2 19B on RX 6750 XT 12GB receives a F grade with 8.5 tok/s and 4K context.

What context window can CogVLM2 19B use on RX 6750 XT 12GB?

On RX 6750 XT 12GB, CogVLM2 19B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RX 6750 XT 12GB See all hardware for CogVLM2 19B

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