MacBook Pro M4 Max 96GBOpción económica
96 GB Unified (+32)
C
Este setup está bastante equilibrado para este modelo.74.3 tok/s decodificación
~$2,499 MSRP
Can Yi 1.5 9B run on NVIDIA A16 64GB?
YES — Runs Great
C51Usable
Estimated from fit model
Yi 1.5 9B needs ~14.6 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~93 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 14.6 GB, 92.7 tok/s, Runs well
14.6 GB required64.0 GB available
23% VRAM used
Fit status
Runs well
Decode
92.7 tok/s
TTFT
2088 ms
Safe context
4K
Memory
14.6 GB / 64.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsYi 1.5 9B on NVIDIA A16 64GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 92.7 tok/s decode · 2.1s TTFT (warm) · 232 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 92.7 tok/s | 1139 ms | 4K |
| Coding | C | Runs well | 92.7 tok/s | 2088 ms | 4K |
| Agentic Coding | C | Runs well | 92.7 tok/s | 3038 ms | 4K |
| Reasoning | C | Runs well | 92.7 tok/s | 2468 ms | 4K |
| RAG | C | Runs well | 92.7 tok/s | 3797 ms | 4K |
Inference speed
Yi 1.5 9B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Yi 1.5 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 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 | 126.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 121.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 110.3 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 91.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 87.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 74.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 74.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 74.3 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 47.5 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 47.1 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 43.6 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 38.3 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 23.3 | Heavy offload |
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.
Quantization options
How Yi 1.5 9B (9B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C45 |
Q3_K_S | 3 | 4.4 GB | Low | C45 |
NVFP4 | 4 | 5.0 GB | Medium | C45 |
Q4_K_M | 4 | 5.5 GB | Medium | C45 |
Q5_K_M | 5 | 6.5 GB | High | C45 |
Q6_K | 6 | 7.4 GB | High | C45 |
Q8_0 | 8 | 9.6 GB | Very High | C46 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C47 |
Get started
Copy-paste commands to run Yi 1.5 9B on your machine.
Run
lms load Yi-1.5-9B-Chat && lms server startOpciones de mejora
Hardware que ejecuta bien Yi 1.5 9B
Frequently asked questions
Can NVIDIA A16 64GB run Yi 1.5 9B?
Yes, NVIDIA A16 64GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 92.7 tok/s.
How much VRAM does Yi 1.5 9B need?
Yi 1.5 9B (9B parameters) requires approximately 14.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 1.5 9B?
The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 1.5 9B run at on NVIDIA A16 64GB?
On NVIDIA A16 64GB, Yi 1.5 9B achieves approximately 92.7 tokens per second decode speed with a time-to-first-token of 2088ms using Q4_K_M quantization.
Can NVIDIA A16 64GB run Yi 1.5 9B for coding?
For coding workloads, Yi 1.5 9B on NVIDIA A16 64GB receives a C grade with 92.7 tok/s and 4K context.
What context window can Yi 1.5 9B use on NVIDIA A16 64GB?
On NVIDIA A16 64GB, Yi 1.5 9B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
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