CogVLM2 19B needs ~14.6 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q3_K_S quantization, expect ~10 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
3.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
7.0 tok/s
TTFT
27656 ms
Safe context
4K
Memory
16.9 GB / 13.0 GB
Offload
20%
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.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
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 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 7.7 tok/s | 13767 ms | 4K |
| Coding | F | Too heavy | 7.0 tok/s | 27656 ms | 4K |
| Agentic Coding | F | Too heavy | 6.0 tok/s | 47074 ms | 4K |
| Reasoning | F | Too heavy | 7.0 tok/s | 32685 ms | 4K |
| RAG | F | Too heavy | 6.0 tok/s | 58842 ms | 4K |
How CogVLM2 19B (19B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.4 GB | Low | A85 |
Q3_K_SBest for your GPU | 3 | 9.3 GB | Low | A84 |
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 99Upgrade options
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.
~$799 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.
~$1,099 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.
~$1,099 MSRP
RTX 5090 Laptop 24GBBiggest leapMakes 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.
Yes, MacBook Pro M3 Pro 18GB can run CogVLM2 19B at Q3_K_S quantization (Very compromised (needs ~1 GB host RAM)). The recommended Q4_K_M requires 16.9 GB which exceeds available memory, but at Q3_K_S it needs only 14.6 GB. Expected decode speed: 9.7 tok/s.
CogVLM2 19B (19B parameters) requires approximately 16.9 GB at Q4_K_M quantization. On MacBook Pro M3 Pro 18GB, it fits at Q3_K_S using 14.6 GB.
The recommended quantization is Q4_K_M, but on MacBook Pro M3 Pro 18GB the best fitting quantization is Q3_K_S, which uses 14.6 GB.
On MacBook Pro M3 Pro 18GB, CogVLM2 19B achieves approximately 9.7 tokens per second decode speed with a time-to-first-token of 19923ms using Q3_K_S quantization.
For coding workloads, CogVLM2 19B on MacBook Pro M3 Pro 18GB receives a F grade with 7.0 tok/s and 4K context.
On MacBook Pro M3 Pro 18GB, CogVLM2 19B can safely use up to 5K tokens of context at Q3_K_S quantization. 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-m3-pro-18gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
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.
Not always. MacBook Pro M3 Pro 18GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.