Intel Arc B580 12GBBest value456 GB/s (+356)
C
Raises estimated decode speed by about 237%.51.3 tok/s decode
Raises estimated decode speed by about 237%.
~$249 MSRP
OpenChat 3.5 7B Qwen v2.0 i1 needs ~7.7 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~15 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
15.2 tok/s
TTFT
12718 ms
Safe context
90K
Memory
7.7 GB / 11.5 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 | C | Runs well | 15.2 tok/s | 6937 ms | 90K |
| Coding | C | Runs well | 15.2 tok/s | 12718 ms | 90K |
| Agentic Coding | C | Runs well | 15.2 tok/s | 18499 ms | 90K |
| Reasoning | C | Runs well | 15.2 tok/s | 15030 ms | 90K |
| RAG | C | Runs well | 15.2 tok/s | 23124 ms | 90K |
How OpenChat 3.5 7B Qwen v2.0 i1 (7B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C49 |
Q3_K_S | 3 | 3.4 GB | Low | C50 |
NVFP4 | 4 |
Copy-paste commands to run OpenChat 3.5 7B Qwen v2.0 i1 on your machine.
Run
lms load hf-mradermacher--openchat-3-5-7b-qwen-v2-0-i1-gguf && lms server startUpgrade options
Intel Arc B580 12GBBest valueRaises estimated decode speed by about 237%.
~$249 MSRP
Raises estimated decode speed by about 68%.
~$1,999 MSRP
Yes, MacBook Air M2 16GB can run OpenChat 3.5 7B Qwen v2.0 i1 with a C grade (Runs well). Expected decode speed: 15.2 tok/s.
OpenChat 3.5 7B Qwen v2.0 i1 (7B parameters) requires approximately 7.7 GB of memory with Q4_K_M quantization.
The recommended quantization for OpenChat 3.5 7B Qwen v2.0 i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M2 16GB, OpenChat 3.5 7B Qwen v2.0 i1 achieves approximately 15.2 tokens per second decode speed with a time-to-first-token of 12718ms using Q4_K_M quantization.
For coding workloads, OpenChat 3.5 7B Qwen v2.0 i1 on MacBook Air M2 16GB receives a C grade with 15.2 tok/s and 90K context.
On MacBook Air M2 16GB, OpenChat 3.5 7B Qwen v2.0 i1 can safely use up to 90K tokens of context. The model's official context limit is —, 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/hf-mradermacher--openchat-3-5-7b-qwen-v2-0-i1-gguf-on-m2-air-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C50 |
Q4_K_M | 4 | 4.3 GB | Medium | C51 |
Q5_K_M | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | C52 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C51 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Not always. MacBook Air M2 16GB 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.