CodeNinja 1.0 OpenChat 7B i1 needs ~33.6 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~98 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
98.0 tok/s
TTFT
1976 ms
Safe context
3.0M
Memory
33.6 GB / 184.3 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 | 98.0 tok/s | 1078 ms | 3.0M |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 3.0M |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 3.0M |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 3.0M |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 3.0M |
How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | D37 |
Q3_K_S | 3 | 3.4 GB | Low | D37 |
NVFP4 | 4 |
Copy-paste commands to run CodeNinja 1.0 OpenChat 7B i1 on your machine.
Run
lms load hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf && lms server startYes, Mac Studio M3 Ultra 256GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 98.0 tok/s.
CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 33.6 GB of memory with Q4_K_M quantization.
The recommended quantization for CodeNinja 1.0 OpenChat 7B i1 is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M3 Ultra 256GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on Mac Studio M3 Ultra 256GB receives a C grade with 98.0 tok/s and 3.0M context.
On Mac Studio M3 Ultra 256GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 3.0M 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--codeninja-1-0-openchat-7b-i1-gguf-on-m3-ultra-256gb" 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 |
| D37 |
Q4_K_M | 4 | 4.3 GB | Medium | D37 |
Q5_K_M | 5 | 5.0 GB | High | D37 |
Q6_K | 6 | 5.7 GB | High | D37 |
Q8_0 | 8 | 7.5 GB | Very High | D37 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | D37 |
Not always. Mac Studio M3 Ultra 256GB 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.