Can CogVLM2 19B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A80Great

Estimated from fit model

CogVLM2 19B needs ~27.7 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~174 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 27.7 GB, 187.0 tok/s, Runs well

27.7 GB required128.0 GB available

22% VRAM used

Fit status

Runs well

Decode

187.0 tok/s

TTFT

1035 ms

Safe context

Memory

27.7 GB / 128.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsCogVLM2 19B on Intel Data Center GPU Max 1550 128GB
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: 187.0 tok/s decode · 1.0s TTFT (warm) · 468 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	187.0 tok/s	565 ms	8K
Coding	A	Runs well	173.9 tok/s	1113 ms	8K
Agentic Coding	A	Runs well	187.0 tok/s	1506 ms	8K
Reasoning	A	Runs well	187.0 tok/s	1224 ms	8K
RAG	A	Runs well	187.0 tok/s	1882 ms	8K

Quantization options

How CogVLM2 19B (19B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	A71
Q3_K_S	3	9.3 GB	Low	A72
NVFP4	4	10.6 GB	Medium	A72
Q4_K_M	4	11.6 GB	Medium	A72
Q5_K_M	5	13.7 GB	High	A72
Q6_K	6	15.6 GB	High	A72
Q8_0	8	20.3 GB	Very High	A72
F16Best for your GPU	16	38.9 GB	Maximum	A75

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 Intel Data Center GPU Max 1550 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	29.2 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	304.8 tok/s
Qwen 3.5 27B	27B	S	132.2 tok/s
Qwen 3.6 27B	27B	S	82.4 tok/s
Qwen 3.5 122B A10B	122B	S	81 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run CogVLM2 19B?

Yes, Intel Data Center GPU Max 1550 128GB can run CogVLM2 19B with a A grade (Runs well). Expected decode speed: 173.9 tok/s.

How much VRAM does CogVLM2 19B need?

CogVLM2 19B (19B parameters) requires approximately 27.7 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 Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, CogVLM2 19B achieves approximately 173.9 tokens per second decode speed with a time-to-first-token of 1113ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run CogVLM2 19B for coding?

For coding workloads, CogVLM2 19B on Intel Data Center GPU Max 1550 128GB receives a A grade with 173.9 tok/s and 8K context.

What context window can CogVLM2 19B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, 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.

What should I upgrade first if CogVLM2 19B feels slow on Intel Data Center GPU Max 1550 128GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB for CogVLM2 19B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Data Center GPU Max 1550 128GB See all hardware for CogVLM2 19B

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