CogVLM2 19B needs ~19.7 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~43 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
Runs well
Decode
43.0 tok/s
TTFT
4502 ms
Safe context
8K
Memory
19.7 GB / 48.0 GB
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 43.0 tok/s | 2455 ms | 8K |
| Coding | A | Runs well | 43.0 tok/s | 4502 ms | 8K |
| Agentic Coding | A | Runs well | 43.0 tok/s | 6548 ms | 8K |
| Reasoning | A | Runs well | 43.0 tok/s | 5320 ms | 8K |
| RAG | A | Runs well | 43.0 tok/s | 8185 ms | 8K |
How CogVLM2 19B (19B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.4 GB | Low | A75 |
Q3_K_S | 3 | 9.3 GB | Low | A76 |
NVFP4 | 4 |
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 |
|---|---|---|---|---|
| 30.5B | S | 70.1 tok/s | ||
| 27B | S | 30.4 tok/s |
Yes, Quadro RTX 8000 48GB can run CogVLM2 19B with a A grade (Runs well). Expected decode speed: 43.0 tok/s.
CogVLM2 19B (19B parameters) requires approximately 19.7 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 Quadro RTX 8000 48GB, CogVLM2 19B achieves approximately 43.0 tokens per second decode speed with a time-to-first-token of 4502ms using Q4_K_M quantization.
For coding workloads, CogVLM2 19B on Quadro RTX 8000 48GB receives a A grade with 43.0 tok/s and 8K context.
On Quadro RTX 8000 48GB, 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-quadro-rtx-8000-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
10.6 GB |
| Medium |
| A76 |
Q4_K_M | 4 | 11.6 GB | Medium | A77 |
Q5_K_M | 5 | 13.7 GB | High | A77 |
Q6_K | 6 | 15.6 GB | High | A78 |
Q8_0 | 8 | 20.3 GB | Very High | A79 |
F16Best for your GPU | 16 | 38.9 GB | Maximum | A81 |
| 27B | S | 23.1 tok/s |
| 35B | S | 58.9 tok/s |
| 30B | S | 72.5 tok/s |