CogVLM2 19B needs ~25.3 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~30 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
38.7 tok/s
TTFT
5005 ms
Safe context
8K
Memory
25.3 GB / 69.1 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 29.7 tok/s | 3558 ms | 8K |
| Coding | A | Runs well | 29.7 tok/s | 6523 ms | 8K |
| Agentic Coding | A | Runs well | 29.7 tok/s | 9488 ms | 8K |
| Reasoning | A | Runs well | 29.7 tok/s | 7709 ms | 8K |
| RAG | A | Runs well | 29.7 tok/s | 11860 ms | 8K |
How CogVLM2 19B (19B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.4 GB | Low | A74 |
Q3_K_S | 3 | 9.3 GB | Low | A74 |
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 |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 52 tok/s | |
| Qwen 3.5 27B | 27B | S | 36.1 tok/s |
Yes, MacBook Pro M4 Max 96GB can run CogVLM2 19B with a A grade (Runs well). Expected decode speed: 29.7 tok/s.
CogVLM2 19B (19B parameters) requires approximately 25.3 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 MacBook Pro M4 Max 96GB, CogVLM2 19B achieves approximately 29.7 tokens per second decode speed with a time-to-first-token of 6523ms using Q4_K_M quantization.
For coding workloads, CogVLM2 19B on MacBook Pro M4 Max 96GB receives a A grade with 29.7 tok/s and 8K context.
On MacBook Pro M4 Max 96GB, 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-m4-max-96gb" 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 |
| A74 |
Q4_K_M | 4 | 11.6 GB | Medium | A74 |
Q5_K_M | 5 | 13.7 GB | High | A75 |
Q6_K | 6 | 15.6 GB | High | A75 |
Q8_0 | 8 | 20.3 GB | Very High | A76 |
F16Best for your GPU | 16 | 38.9 GB | Maximum | A81 |
| Qwen 3.6 27B | 27B | S | 27.4 tok/s |
| Qwen 3.6 35B A3B | 35B | S | 43.7 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 53.8 tok/s |
Not always. MacBook Pro M4 Max 96GB 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.