CogVLM2 19B needs ~15.7 GB but RX 5700 XT 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
7.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.9 tok/s
TTFT
49594 ms
Safe context
4K
Memory
15.7 GB / 8.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 15.7 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 4.6 tok/s | 22822 ms | 4K |
| Coding | F | Too heavy | 3.9 tok/s | 49594 ms | 4K |
| Agentic Coding | F | Too heavy | 3.2 tok/s | 86883 ms | 4K |
| Reasoning | F | Too heavy | 3.9 tok/s | 58611 ms | 4K |
| RAG | F | Too heavy | 3.2 tok/s | 108604 ms | 4K |
How CogVLM2 19B (19B params) fits at each quantization level on RX 5700 XT 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.4 GB | Low | F0 |
Q3_K_S | 3 | 9.3 GB | Low | F0 |
NVFP4 | 4 | 10.6 GB | Medium | F0 |
Q4_K_M | 4 | 11.6 GB | Medium | F0 |
Q5_K_M | 5 | 13.7 GB | High | F0 |
Q6_K | 6 | 15.6 GB | High | F0 |
Q8_0 | 8 | 20.3 GB | Very High | F0 |
F16 | 16 | 38.9 GB | Maximum | F0 |
Opções de upgrade
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
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
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 24GBMaior saltoMakes 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.
No, CogVLM2 19B requires more memory than RX 5700 XT 8GB provides.
CogVLM2 19B (19B parameters) requires approximately 15.7 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 RX 5700 XT 8GB, CogVLM2 19B achieves approximately 3.9 tokens per second decode speed with a time-to-first-token of 49594ms using Q4_K_M quantization.
For coding workloads, CogVLM2 19B on RX 5700 XT 8GB receives a F grade with 3.9 tok/s and 4K context.
On RX 5700 XT 8GB, CogVLM2 19B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/cogvlm2-19b-on-rx-5700-xt-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
