RX 7900 XTX 24GBBest value
960 GB/s (+560)
C
Raises estimated decode speed by about 80%.98 tok/s decode
Raises estimated decode speed by about 80%.
~$999 MSRP
OpenChat 3.5 7B Qwen v2.0 i1 needs ~9.4 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~54 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
54.3 tok/s
TTFT
3563 ms
Safe context
281K
Memory
9.4 GB / 23.0 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 | 54.3 tok/s | 1944 ms | 281K |
| Coding | C | Runs well | 54.3 tok/s | 3563 ms | 281K |
| Agentic Coding | C | Runs well | 54.3 tok/s | 5183 ms | 281K |
| Reasoning | C | Runs well | 54.3 tok/s | 4211 ms | 281K |
| RAG | C | Runs well | 54.3 tok/s | 6479 ms | 281K |
How OpenChat 3.5 7B Qwen v2.0 i1 (7B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
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
Yes, MacBook Pro M2 Max 32GB can run OpenChat 3.5 7B Qwen v2.0 i1 with a C grade (Runs well). Expected decode speed: 54.3 tok/s.
OpenChat 3.5 7B Qwen v2.0 i1 (7B parameters) requires approximately 9.4 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 Pro M2 Max 32GB, OpenChat 3.5 7B Qwen v2.0 i1 achieves approximately 54.3 tokens per second decode speed with a time-to-first-token of 3563ms using Q4_K_M quantization.
For coding workloads, OpenChat 3.5 7B Qwen v2.0 i1 on MacBook Pro M2 Max 32GB receives a C grade with 54.3 tok/s and 281K context.
On MacBook Pro M2 Max 32GB, OpenChat 3.5 7B Qwen v2.0 i1 can safely use up to 281K 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-max-32gb" 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 |
| C45 |
Q4_K_M | 4 | 4.3 GB | Medium | C45 |
Q5_K_M | 5 | 5.0 GB | High | C45 |
Q6_K | 6 | 5.7 GB | High | C46 |
Q8_0 | 8 | 7.5 GB | Very High | C47 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C50 |
Not always. MacBook Pro M2 Max 32GB 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.