CogVLM2 19B needs ~17.5 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~6 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
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
6.1 tok/s
TTFT
31732 ms
Safe context
8K
Memory
17.5 GB / 17.3 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 6.3 tok/s | 16743 ms | 8K |
| Coding | A | Runs with offload | 5.7 tok/s | 34112 ms | 8K |
| Agentic Coding | B | Very compromised (needs ~1.6 GB host RAM) | 5.0 tok/s | 55870 ms | 8K |
| Reasoning | A | Runs with offload (needs ~0.2 GB host RAM) | 6.1 tok/s | 37501 ms | 8K |
| RAG | B | Very compromised (needs ~1.6 GB host RAM) | 5.0 tok/s | 69838 ms |
How CogVLM2 19B (19B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.4 GB | Low | A84 |
Q3_K_S | 3 | 9.3 GB | Low | A84 |
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 |
|---|---|---|---|---|
 Magistral Small 2507 | 24B | B | 3.8 tok/s | |
| Devstral Small 2 24B Instruct | 24B | B | 3.8 tok/s |
Yes, MacBook Air M3 24GB can run CogVLM2 19B with a A grade (Runs with offload). Expected decode speed: 5.7 tok/s.
CogVLM2 19B (19B parameters) requires approximately 17.5 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 Air M3 24GB, CogVLM2 19B achieves approximately 5.7 tokens per second decode speed with a time-to-first-token of 34112ms using Q4_K_M quantization.
For coding workloads, CogVLM2 19B on MacBook Air M3 24GB receives a A grade with 5.7 tok/s and 8K context.
On MacBook Air M3 24GB, 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-m3-air-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
10.6 GB |
| Medium |
| A84 |
Q4_K_MBest for your GPU | 4 | 11.6 GB | Medium | A84 |
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 |
| Devstral Small 1.1 | 24B | B | 3.8 tok/s |
 GPT-OSS 20B | 21B | A | 11.4 tok/s |
| Codestral 2 25.08 | 22B | B | 4.3 tok/s |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. MacBook Air M3 24GB 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.