Yi 1.5 6B Chat needs ~19.1 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~84 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
84.0 tok/s
TTFT
2305 ms
Safe context
1.7M
Memory
19.1 GB / 92.2 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 | 84.0 tok/s | 1257 ms | 1.7M |
| Coding | C | Runs well | 84.0 tok/s | 2305 ms | 1.7M |
| Agentic Coding | C | Runs well | 84.0 tok/s | 3352 ms | 1.7M |
| Reasoning | C | Runs well | 84.0 tok/s | 2724 ms | 1.7M |
| RAG | C | Runs well | 84.0 tok/s | 4190 ms | 1.7M |
Inference speed
Estimated decode speed (tokens/sec) for Yi 1.5 6B Chat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~114 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 114.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 84.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 84.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 84.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 84.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 84.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 84.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 84.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 84.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 84.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 84.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 65.6 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 64.9 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 60.1 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 54.3 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 52.8 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Yi 1.5 6B Chat (6B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | D39 |
Q3_K_S | 3 | 2.9 GB | Low | D39 |
NVFP4 | 4 | 3.4 GB | Medium | D39 |
Q4_K_M | 4 | 3.7 GB | Medium | D39 |
Q5_K_M | 5 | 4.3 GB | High | D39 |
Q6_K | 6 | 4.9 GB | High | D39 |
Q8_0 | 8 | 6.4 GB | Very High | D39 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | D40 |
Copy-paste commands to run Yi 1.5 6B Chat on your machine.
Run
lms load hf-maziyarpanahi--yi-1-5-6b-chat-gguf && lms server startYes, Mac Studio M2 Ultra 128GB can run Yi 1.5 6B Chat with a C grade (Runs well). Expected decode speed: 84.0 tok/s.
Yi 1.5 6B Chat (6B parameters) requires approximately 19.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 6B Chat is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M2 Ultra 128GB, Yi 1.5 6B Chat achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 6B Chat on Mac Studio M2 Ultra 128GB receives a C grade with 84.0 tok/s and 1.7M context.
On Mac Studio M2 Ultra 128GB, Yi 1.5 6B Chat can safely use up to 1.7M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. Mac Studio M2 Ultra 128GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-1-5-6b-chat-gguf-on-m2-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: