CogVLM2 19B needs ~16.9 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~46 tok/s.
Operating mode
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.
Select quantization to explore
Fit status
Tight fit
Decode
46.3 tok/s
TTFT
4181 ms
Safe context
8K
Memory
16.9 GB / 20.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 46.3 tok/s | 2281 ms | 8K |
| Coding | A | Tight fit | 46.3 tok/s | 4181 ms | 8K |
| Agentic Coding | A | Runs with offload | 46.3 tok/s | 6082 ms | 8K |
| Reasoning | A | Tight fit | 46.3 tok/s | 4941 ms | 8K |
| RAG | A | Runs with offload | 46.3 tok/s | 7602 ms | 8K |
How CogVLM2 19B (19B params) fits at each quantization level on RTX A4500 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 |
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 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 42.3 tok/s | |
| Qwen 3.5 27B | 27B | A | 19.1 tok/s | |
| Qwen 3.6 27B | 27B | S | 18 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | A | 45 tok/s | |
 Magistral Small 2507 | 24B | S | 36.7 tok/s |
Yes, RTX A4500 20GB can run CogVLM2 19B with a A grade (Tight fit). Expected decode speed: 46.3 tok/s.
CogVLM2 19B (19B parameters) requires approximately 16.9 GB of memory with Q4_K_M quantization.
The recommended quantization for CogVLM2 19B is Q4_K_M, which balances quality and memory efficiency.
On RTX A4500 20GB, CogVLM2 19B achieves approximately 46.3 tokens per second decode speed with a time-to-first-token of 4181ms using Q4_K_M quantization.
For coding workloads, CogVLM2 19B on RTX A4500 20GB receives a A grade with 46.3 tok/s and 8K context.
On RTX A4500 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/cogvlm2-19b-on-rtx-a4500-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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